Word-sense disambiguation - Knowledge (XXG)

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the greatest word overlap in their dictionary definitions. For example, when disambiguating the words in "pine cone", the definitions of the appropriate senses both include the words evergreen and tree (at least in one dictionary). A similar approach searches for the shortest path between two words: the second word is iteratively searched among the definitions of every semantic variant of the first word, then among the definitions of every semantic variant of each word in the previous definitions and so on. Finally, the first word is disambiguated by selecting the semantic variant which minimizes the distance from the first to the second word.

270:. WSD systems are normally tested by having their results on a task compared against those of a human. However, while it is relatively easy to assign parts of speech to text, training people to tag senses has been proven to be far more difficult. While users can memorize all of the possible parts of speech a word can take, it is often impossible for individuals to memorize all of the senses a word can take. Moreover, humans do not agree on the task at hand – give a list of senses and sentences, and humans will not always agree on which word belongs in which sense. 651:(i.e., short defining gloss and one or more usage example) using a pre-trained word-embedding model. These centroids are later used to select the word sense with the highest similarity of a target word to its immediately adjacent neighbors (i.e., predecessor and successor words). After all words are annotated and disambiguated, they can be used as a training corpus in any standard word-embedding technique. In its improved version, MSSA can make use of word sense embeddings to repeat its disambiguation process iteratively. 643:) objects as nodes and the relationship between nodes as edges. The relations (edges) in AutoExtend can either express the addition or similarity between its nodes. The former captures the intuition behind the offset calculus, while the latter defines the similarity between two nodes. In MSSA, an unsupervised disambiguation system uses the similarity between word senses in a fixed context window to select the most suitable word sense using a pre-trained word-embedding model and 635:) can also assist unsupervised systems in mapping words and their senses as dictionaries. Some techniques that combine lexical databases and word embeddings are presented in AutoExtend and Most Suitable Sense Annotation (MSSA). In AutoExtend, they present a method that decouples an object input representation into its properties, such as words and their word senses. AutoExtend uses a graph structure to map words (e.g. text) and non-word (e.g. 623:) has become one of the most fundamental blocks in several NLP systems. Even though most of traditional word-embedding techniques conflate words with multiple meanings into a single vector representation, they still can be used to improve WSD. A simple approach to employ pre-computed word embeddings to represent word senses is to compute the centroids of sense clusters. In addition to word-embedding techniques, lexical databases (e.g., 980: 112:) level is routinely above 90% (as of 2009), with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively. 563:, using any supervised method. This classifier is then used on the untagged portion of the corpus to extract a larger training set, in which only the most confident classifications are included. The process repeats, each new classifier being trained on a successively larger training corpus, until the whole corpus is consumed, or until a given maximum number of iterations is reached. 855:(2007). The objective of the competition is to organize different lectures, preparing and hand-annotating corpus for testing systems, perform a comparative evaluation of WSD systems in several kinds of tasks, including all-words and lexical sample WSD for different languages, and, more recently, new tasks such as 345:– was proposed as a possible solution to the sense discreteness problem. The task consists of providing a substitute for a word in context that preserves the meaning of the original word (potentially, substitutes can be chosen from the full lexicon of the target language, thus overcoming discreteness). 453:

is the seminal dictionary-based method. It is based on the hypothesis that words used together in text are related to each other and that the relation can be observed in the definitions of the words and their senses. Two (or more) words are disambiguated by finding the pair of dictionary senses with

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frequently discover in corpora loose and overlapping word meanings, and standard or conventional meanings extended, modulated, and exploited in a bewildering variety of ways. The art of lexicography is to generalize from the corpus to definitions that evoke and explain the full range of meaning of a

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A task-independent sense inventory is not a coherent concept: each task requires its own division of word meaning into senses relevant to the task. Additionally, completely different algorithms might be required by different applications. In machine translation, the problem takes the form of target

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Both WSD and part-of-speech tagging involve disambiguating or tagging with words. However, algorithms used for one do not tend to work well for the other, mainly because the part of speech of a word is primarily determined by the immediately adjacent one to three words, whereas the sense of a word

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and sense tagging have proven to be very closely related, with each potentially imposing constraints upon the other. The question whether these tasks should be kept together or decoupled is still not unanimously resolved, but recently scientists incline to test these things separately (e.g. in the

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of language examples is also required). WSD task has two variants: "lexical sample" (disambiguating the occurrences of a small sample of target words which were previously selected) and "all words" task (disambiguation of all the words in a running text). "All words" task is generally considered a

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Knowledge is a fundamental component of WSD. Knowledge sources provide data which are essential to associate senses with words. They can vary from corpora of texts, either unlabeled or annotated with word senses, to machine-readable dictionaries, thesauri, glossaries, ontologies, etc. They can be

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to a set of dictionary senses is not desired, cluster-based evaluations (including measures of entropy and purity) can be performed. Alternatively, word sense induction methods can be tested and compared within an application. For instance, it has been shown that word sense induction improves Web

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will provide different divisions of words into senses. Some researchers have suggested choosing a particular dictionary, and using its set of senses to deal with this issue. Generally, however, research results using broad distinctions in senses have been much better than those using narrow ones.

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have been shown to be the most successful approaches, to date, probably because they can cope with the high-dimensionality of the feature space. However, these supervised methods are subject to a new knowledge acquisition bottleneck since they rely on substantial amounts of manually sense-tagged

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Shallow approaches do not try to understand the text, but instead consider the surrounding words. These rules can be automatically derived by the computer, using a training corpus of words tagged with their word senses. This approach, while theoretically not as powerful as deep approaches, gives

332:, and disagreements arise. For example, in Senseval-2, which used fine-grained sense distinctions, human annotators agreed in only 85% of word occurrences. Word meaning is in principle infinitely variable and context-sensitive. It does not divide up easily into distinct or discrete sub-meanings. 368:

and her colleagues, at the Cambridge Language Research Unit in England, in the 1950s. This attempt used as data a punched-card version of Roget's Thesaurus and its numbered "heads", as an indicator of topics and looked for repetitions in text, using a set intersection algorithm. It was not very

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or discrimination. Then, new occurrences of the word can be classified into the closest induced clusters/senses. Performance has been lower than for the other methods described above, but comparisons are difficult since senses induced must be mapped to a known dictionary of word senses. If a

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evaluation task is also focused on WSD across 2 or more languages simultaneously. Unlike the Multilingual WSD tasks, instead of providing manually sense-annotated examples for each sense of a polysemous noun, the sense inventory is built up on the basis of parallel corpora, e.g. Europarl

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was an early example of such an algorithm. It uses the ‘One sense per collocation’ and the ‘One sense per discourse’ properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and in a given collocation.

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more realistic form of evaluation, but the corpus is more expensive to produce because human annotators have to read the definitions for each word in the sequence every time they need to make a tagging judgement, rather than once for a block of instances for the same target word.

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approach starts from a small amount of seed data for each word: either manually tagged training examples or a small number of surefire decision rules (e.g., 'play' in the context of 'bass' almost always indicates the musical instrument). The seeds are used to train an initial

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Comparing and evaluating different WSD systems is extremely difficult, because of the different test sets, sense inventories, and knowledge resources adopted. Before the organization of specific evaluation campaigns most systems were assessed on in-house, often small-scale,

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research of the early days of AI research have been applied with some success. More complex graph-based approaches have been shown to perform almost as well as supervised methods or even outperforming them on specific domains. Recently, it has been reported that simple

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may be determined by words further away. The success rate for part-of-speech tagging algorithms is at present much higher than that for WSD, state-of-the art being around 96% accuracy or better, as compared to less than 75% accuracy in word sense disambiguation with

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is trained for each distinct word on a corpus of manually sense-annotated examples, and completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful

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Classical WSD for other languages uses their respective WordNet as sense inventories and sense annotated corpora tagged in their respective languages. Often researchers will also tapped on the SemCor corpus and aligned bitexts with English as its

306:– that is, 'edge of river'). In information retrieval, a sense inventory is not necessarily required, because it is enough to know that a word is used in the same sense in the query and a retrieved document; what sense that is, is unimportant. 923:

as multilingual sense inventory. It evolved from the Translation WSD evaluation tasks that took place in Senseval-2. A popular approach is to carry out monolingual WSD and then map the source language senses into the corresponding target word

360:. These approaches are generally not considered to be very successful in practice, mainly because such a body of knowledge does not exist in a computer-readable format, outside very limited domains. Additionally due to the long tradition in 863:, etc. The systems submitted for evaluation to these competitions usually integrate different techniques and often combine supervised and knowledge-based methods (especially for avoiding bad performance in lack of training examples). 497:

The use of selectional preferences (or selectional restrictions) is also useful, for example, knowing that one typically cooks food, one can disambiguate the word bass in "I am cooking basses" (i.e., it's not a musical instrument).

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implement simple and robust IR techniques that can successfully mine the Web for information to use in WSD. The historic lack of training data has provoked the appearance of some new algorithms and techniques, as described in

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In recent years , the WSD evaluation task choices had grown and the criterion for evaluating WSD has changed drastically depending on the variant of the WSD evaluation task. Below enumerates the variety of WSD tasks:

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in Hindi have hindered the performance of supervised models of WSD, while the unsupervised models suffer due to extensive morphology. A possible solution to this problem is the design of a WSD model by means of

177:, semi-supervised and unsupervised corpus-based systems, combinations of different methods, and the return of knowledge-based systems via graph-based methods. Still, supervised systems continue to perform best. 828:. In order to test one's algorithm, developers should spend their time to annotate all word occurrences. And comparing methods even on the same corpus is not eligible if there is different sense inventories. 2195:. Proc. of seventh International Workshop on Semantic Evaluation (SemEval), in the Second Joint Conference on Lexical and Computational Semantics (*SEM 2013), Atlanta, USA, June 14–15th, 2013, pp. 222–231. 2722:. Proc. of the 44th Annual Meeting of the Association for Computational Linguistics joint with the 21st International Conference on Computational Linguistics. Sydney, Australia: COLING-ACL. Archived from 364:, of trying such approaches in terms of coded knowledge and in some cases, it can be hard to distinguish between knowledge involved in linguistic or world knowledge. The first attempt was that by 494:

from Knowledge (XXG) to WordNet has been shown to boost simple knowledge-based methods, enabling them to rival the best supervised systems and even outperform them in a domain-specific setting.

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depend crucially on the existence of manually annotated examples for every word sense, a requisite that can so far be met only for a handful of words for testing purposes, as it is done in the

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WSD was first formulated as a distinct computational task during the early days of machine translation in the 1940s, making it one of the oldest problems in computational linguistics.

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word selection. The "senses" are words in the target language, which often correspond to significant meaning distinctions in the source language ("bank" could translate to the French

3287: 745:, to acquire lexical information automatically. WSD has been traditionally understood as an intermediate language engineering technology which could improve applications such as 167:(OALD), became available: hand-coding was replaced with knowledge automatically extracted from these resources, but disambiguation was still knowledge-based or dictionary-based. 964:

UKB: Graph Base WSD, a collection of programs for performing graph-based Word Sense Disambiguation and lexical similarity/relatedness using a pre-existing Lexical Knowledge Base

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search result clustering by increasing the quality of result clusters and the degree diversification of result lists. It is hoped that unsupervised learning will overcome the

2568:. Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. Rochester, New York: HLT-NAACL. 170:

In the 1990s, the statistical revolution advanced computational linguistics, and WSD became a paradigm problem on which to apply supervised machine learning techniques.

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is the greatest challenge for WSD researchers. The underlying assumption is that similar senses occur in similar contexts, and thus senses can be induced from text by

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Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing

393:: These make use of a secondary source of knowledge such as a small annotated corpus as seed data in a bootstrapping process, or a word-aligned bilingual corpus. 3425: 755: 927: 160:' preference semantics. However, since WSD systems were at the time largely rule-based and hand-coded they were prone to a knowledge acquisition bottleneck. 2215:. In EACL-2006 Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together, pages 33–40, Trento, Italy, April 2006. 2155:. In EACL-2006 Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together, pages 33–40, Trento, Italy, April 2006. 570:

information that supplements the tagged corpora. These techniques have the potential to help in the adaptation of supervised models to different domains.

2932:. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. EMNLP-CoNLL. 875:

As technology evolves, the Word Sense Disambiguation (WSD) tasks grows in different flavors towards various research directions and for more languages:

2943:. Proc. of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3). Barcelona, Spain. Archived from 573:

Also, an ambiguous word in one language is often translated into different words in a second language depending on the sense of the word. Word-aligned

441:. Graph-based approaches have also gained much attention from the research community, and currently achieve performance close to the state of the art. 405:: These eschew (almost) completely external information and work directly from raw unannotated corpora. These methods are also known under the name of 3836: 3280: 1130: 2898:. Proc. of Semeval-2007 Workshop (SEMEVAL), in the 45th Annual Meeting of the Association for Computational Linguistics. Prague, Czech Republic: ACL. 337:

word, making it seem like words are well-behaved semantically. However, it is not at all clear if these same meaning distinctions are applicable in

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sets (e.g. the concept of car is encoded as { car, auto, automobile, machine, motorcar }). Other resources used for disambiguation purposes include

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successful, but had strong relationships to later work, especially Yarowsky's machine learning optimisation of a thesaurus method in the 1990s.

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Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

647:. For each context window, MSSA calculates the centroid of each word sense definition by averaging the word vectors of its words in WordNet's 2095: 2003: 1446: 1349: 1270: 961:

WordNet::SenseRelate, a project that includes free, open source systems for word sense disambiguation and lexical sample sense disambiguation

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are deemed unnecessary). Probably every machine learning algorithm going has been applied to WSD, including associated techniques such as

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In the 1970s, WSD was a subtask of semantic interpretation systems developed within the field of artificial intelligence, starting with

2060:. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2002. 2040:. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2004. 1122: 919:

evaluation tasks focused on WSD across 2 or more languages simultaneously, using their respective WordNets as its sense inventories or

4046: 4036: 3607: 3017: 2426: 2175:. Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. June 04-04, 2009, Boulder, Colorado. 2033: 1512:

Mikolov, Tomas; Chen, Kai; Corrado, Greg; Dean, Jeffrey (2013-01-16). "Efficient Estimation of Word Representations in Vector Space".

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Diamantini, C.; Mircoli, A.; Potena, D.; Storti, E. (2015-06-01). "Semantic disambiguation in a social information discovery system".

2679: 2080:. In International Symposium on Machine Translation, Natural Language Processing and Translation Support Systems, Delhi, India, 2004. 3761: 3592: 2938: 916: 909: 285:

distinctions, so this again is why research on coarse-grained distinctions has been put to test in recent WSD evaluation exercises.

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Ruas, Terry; Grosky, William; Aizawa, Akiko (December 2019). "Multi-sense embeddings through a word sense disambiguation process".

2687:. Proc. of the North American Chapter of the Association for Computational Linguistics. Rochester, New York: NAACL. Archived from 3532: 1013: 164: 153:(1960) argued that WSD could not be solved by "electronic computer" because of the need in general to model all world knowledge. 67: 2869: 2168: 955:

BabelNet API, a Java API for knowledge-based multilingual Word Sense Disambiguation in 6 different languages using the BabelNet

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Edmonds, Philip; Kilgarriff, Adam (2002). "Introduction to the special issue on evaluating word sense disambiguation systems".

2743:. Proc. of the 2010 Conference on Empirical Methods in Natural Language Processing. MIT Stata Center, Massachusetts, US: EMNLP. 2708:. Proceedings of the 11th Conference on European chapter of the Association for Computational Linguistics. Trento, Italy: EACL. 673: 486:, perform state-of-the-art WSD in the presence of a sufficiently rich lexical knowledge base. Also, automatically transferring 31: 92:

Many techniques have been researched, including dictionary-based methods that use the knowledge encoded in lexical resources,

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Accuracy of current algorithms is difficult to state without a host of caveats. In English, accuracy at the coarse-grained (

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Given that natural language requires reflection of neurological reality, as shaped by the abilities provided by the brain's

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Buitelaar, P.; Magnini, B.; Strapparava, C.; Vossen, P. (2006). "Domain-specific WSD". In Agirre, E.; Edmonds, P. (eds.).

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methods are based on the assumption that the context can provide enough evidence on its own to disambiguate words (hence,

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has paved way for several Supervised methods which have been proven to produce a higher accuracy in disambiguating nouns.

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The 2000s saw supervised techniques reach a plateau in accuracy, and so attention has shifted to coarser-grained senses,

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Rothe, Sascha; Schütze, Hinrich (2015). "AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes".

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Pennington, Jeffrey; Socher, Richard; Manning, Christopher (2014). "Glove: Global Vectors for Word Representation".

4056: 3641: 3612: 3390: 1757:. Association for Computational Linguistics and the International Joint Conference on Natural Language Processing. 852: 2585:

Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone

2540:. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Barcelona, Spain: EMNLP. 938:

data, consisting of polysemous words and the sentence that they occurred in, then WSD is performed on a different

3337: 2554:. Proceedings of ACL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions. Philadelphia. 2299: 406: 361: 78: 2375: 1732: 1692: 4010: 3934: 3666: 3622: 3507: 3405: 1623:

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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In order to define common evaluation datasets and procedures, public evaluation campaigns have been organized.

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rely on knowledge about word senses, which is only sparsely formulated in dictionaries and lexical databases.

2735: 2349: 3914: 3884: 3551: 2805:), in the 45th Annual Meeting of the Association for Computational Linguistics. Prague, Czech Republic: ACL. 1003: 879: 526: 264: 1719:

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics

3771: 3464: 3442: 3432: 3400: 3375: 2863:. Proceedings of the 2nd Workshop on Scalable Natural Language Understanding Systems in HLT/NAACL. Boston. 2497: 856: 628: 434: 422: 239: 63: 59: 2811: 2643: 341:, as the decisions of lexicographers are usually driven by other considerations. In 2009, a task – named 3631: 3204:"Distinguishing systems and distinguishing senses: New evaluation methods for word sense disambiguation" 2877:. Proc. of the 48th Annual Meeting of the Association for Computational Linguistics. ACL. Archived from 2749: 2324: 952:

Babelfy, a unified state-of-the-art system for multilingual Word Sense Disambiguation and Entity Linking

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Bhingardive, Sudha; Singh, Dhirendra; V, Rudramurthy; Redkar, Hanumant; Bhattacharyya, Pushpak (2015).

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first introduced the problem in a computational context in his 1949 memorandum on translation. Later,

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corpora have been used to infer cross-lingual sense distinctions, a kind of semi-supervised system.

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content words around each word to be disambiguated in the corpus, and statistically analyzing those

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Agirre, E.; Stevenson, M. (2007). "Knowledge sources for WSD". In Agirre, E.; Edmonds, P. (eds.).

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Constraint-based Grammar Formalisms: Parsing and Type Inference for Natural and Computer Languages

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as it sense inventory and the primary classification input is normally based on the SemCor corpus.

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Word-sense disambiguation using statistical models of Roget's categories trained on large corpora

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Lucia Specia, Maria das Gracas Volpe Nunes, Gabriela Castelo Branco Ribeiro, and Mark Stevenson.

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Lucia Specia, Maria das Gracas Volpe Nunes, Gabriela Castelo Branco Ribeiro, and Mark Stevenson.

2070: 1890: 1862: 1788: 1762: 1761:. Stroudsburg, Pennsylvania, USA: Association for Computational Linguistics. pp. 1793–1803. 1722: 1684: 1579: 1548: 1513: 1355: 1164: 985: 738: 722:

The knowledge acquisition bottleneck is perhaps the major impediment to solving the WSD problem.

648: 599: 547: 365: 125: 839:) is an international word sense disambiguation competition, held every three years since 1998: 2812:"Structural Semantic Interconnections: a Knowledge-Based Approach to Word Sense Disambiguation" 2609:. Proceedings of the 2nd Conference on Language Resources and Evaluation. Athens, Greece: LREC. 2464: 3979: 3691: 3499: 3410: 3013: 2834: 2772: 2422: 1999: 1832: 1599: 1535:

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

1442: 1434: 1345: 894: 750: 608: 522: 518: 491: 174: 81:, computer science has had a long-term challenge in developing the ability in computers to do 2591:. Proc. of SIGDOC-86: 5th International Conference on Systems Documentation. Toronto, Canada. 2583: 3856: 3741: 3716: 3517: 3420: 3215: 3182: 3137: 3101: 3036: 2826: 2764: 2658: 2630: 2476: 1880: 1872: 1822: 1780: 1676: 1636: 1626: 1589: 1538: 1337: 1156: 1118: 956: 706: 701: 595: 86: 2924: 2596:

Litkowski, K. C. (2005). "Computational lexicons and dictionaries". In Brown, K. R. (ed.).

2458:. Proceedings of the 20th National Conference on Artificial Intelligence. Pittsburgh: AAAI. 2030: 383:- and knowledge-based methods: These rely primarily on dictionaries, thesauri, and lexical 3968: 3929: 3924: 3792: 3522: 3395: 3370: 3352: 2688: 2560: 2232: 2212: 2192: 2172: 2152: 2077: 2057: 2037: 1126: 3057: 2944: 190:

One problem with word sense disambiguation is deciding what the senses are, as different

2750:"An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation" 2403:"Knowledge-based WSD on Specific Domains: Performing better than Generic Supervised WSD" 1776: 3676: 3656: 3380: 2402: 2069:

Manish Sinha, Mahesh Kumar, Prabhakar Pande, Laxmi Kashyap, and Pushpak Bhattacharyya.

1537:. Stroudsburg, PA, USA: Association for Computational Linguistics. pp. 1532–1543. 998: 742: 679: 620: 450: 430: 384: 319: 278: 2465:"Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction" 4030: 3939: 3751: 3731: 3512: 2904: 2890: 2878: 2793: 2615: 2532: 2510:

Proc. of ANLP-97 Workshop on Tagging Text with Lexical Semantics: Why, What, and How?

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Other semi-supervised techniques use large quantities of untagged corpora to provide

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language independent NLU combining Patom Theory and RRG (Role and Reference Grammar)

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Because of the lack of training data, many word sense disambiguation algorithms use

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Bojanowski, Piotr; Grave, Edouard; Joulin, Armand; Mikolov, Tomas (December 2017).

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Yarowsky, David (2001). "Word sense disambiguation". In Dale; et al. (eds.).

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surrounding words. Two shallow approaches used to train and then disambiguate are

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competitions parts of speech are provided as input for the text to disambiguate).

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Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance

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Semeval-2007 task 02: evaluating word sense induction and discrimination systems

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Representing words considering their context through fixed-size dense vectors (

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An alternative to the use of the definitions is to consider general word-sense

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There are two main approaches to WSD – deep approaches and shallow approaches.

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level (e.g., pen as writing instrument or enclosure), but go down one level to

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2015 International Conference on Collaboration Technologies and Systems (CTS)

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pyWSD, python implementations of Word Sense Disambiguation (WSD) technologies

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evaluation tasks use WordNet as the sense inventory and are largely based on

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superior results in practice, due to the computer's limited world knowledge.

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of each pair of word senses based on a given lexical knowledge base such as

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for computer performance. Human performance, however, is much better on

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Iacobacci, Ignacio; Pilehvar, Mohammad Taher; Navigli, Roberto (2016).

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as a reference sense inventory for English. WordNet is a computational

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Unsupervised domain relevance estimation for word sense disambiguation

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Unsupervised sense disambiguation using bilingual probabilistic models

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One of the most promising trends in WSD research is using the largest

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Computational Linguistics Special Issue on Word Sense Disambiguation

2983:. Proc. of the 14th conference on Computational linguistics. COLING. 2871:

Knowledge-rich Word Sense Disambiguation rivaling supervised systems

2051:

An unsupervised method for word sense tagging using parallel corpora

1668: 848: 1867: 1767: 1727: 1584: 534:

corpora for training, which are laborious and expensive to create.

4015: 3651: 2991:

Unsupervised word sense disambiguation rivaling supervised methods

2345: 1713:

Butnaru, Andrei; Ionescu, Radu Tudor; Hristea, Florentina (2017).

1669:"Unsupervised Most Frequent Sense Detection using Word Embeddings" 1518: 1119:

Entity Linking meets Word Sense Disambiguation: a Unified Approach

696: 1919: 930:

is a combined task evaluation where the sense inventory is first

3537: 55: 45: 3269: 2892:

SemEval-2007 Task 17: English lexical sample, SRL and all words

1811:"AutoExtend: Combining Word Embeddings with Semantic Resources" 1619:"Embeddings for Word Sense Disambiguation: An Evaluation Study" 3812: 2819:

IEEE Transactions on Pattern Analysis and Machine Intelligence

2757:

IEEE Transactions on Pattern Analysis and Machine Intelligence

2681:

Using Knowledge (XXG) for Automatic Word Sense Disambiguation

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Transactions of the Association for Computational Linguistics

37:

For information on Knowledge (XXG) disambiguation pages, see

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Inducing Word Senses to Improve Web Search Result Clustering

2186:

SemEval-2013 Task 12: Multilingual Word Sense Disambiguation

2166:

SemEval-2010 task 3: cross-lingual word sense disambiguation

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bottleneck because they are not dependent on manual effort.

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SemEval-2007 Task 07: Coarse-Grained English All-Words Task

1931: 1384: 2614:

McCarthy, D.; Koeling, R.; Weeds, J.; Carroll, J. (2007).

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classification with the manually sense annotated corpora:

399:: These make use of sense-annotated corpora to train from. 356:

Deep approaches presume access to a comprehensive body of

124:

to specify the senses which are to be disambiguated and a

1907: 1149:

Wiley Interdisciplinary Reviews: Computational Statistics

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Almost all these approaches work by defining a window of

3123:"Introduction to the special issue on the Web as corpus" 2889:

Pradhan, S.; Loper, E.; Dligach, D.; Palmer, M. (2007).

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Different sense granularities for different applications

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sfn error: no target: CITEREFKilgarrifGrefenstette2003 (

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Bhattacharya, Indrajit, Lise Getoor, and Yoshua Bengio.

659:

Other approaches may vary differently in their methods:

206:

Most research in the field of WSD is performed by using

163:

By the 1980s large-scale lexical resources, such as the

2923:

Snow, R.; Prakash, S.; Jurafsky, D.; Ng, A. Y. (2007).

2456:

Scaling up word sense disambiguation via parallel texts

2270: 749:(IR). In this case, however, the reverse is also true: 165:

Oxford Advanced Learner's Dictionary of Current English

3159:

Foundations of Statistical Natural Language Processing

3010:

Word Sense Disambiguation: Algorithms and Applications

2447:

Word Sense Disambiguation: Algorithms and Applications

2419:

Word Sense Disambiguation: Algorithms and Applications

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As human performance serves as the standard, it is an

2616:"Unsupervised acquisition of predominant word senses" 2089: 1228: 30:"Disambiguation" redirects here. For other uses, see 27:

Identification of which sense of a word is being used

2970:

Electric Words: dictionaries, computers and meanings

2401:

Agirre, E.; Lopez de Lacalle, A.; Soroa, A. (2009).

546:, which allows both labeled and unlabeled data. The 3993: 3948: 3903: 3875: 3835: 3780: 3702: 3690: 3621: 3578: 3550: 3498: 3361: 3303: 2500:(Tech. note). Brighton, UK: University of Brighton. 3157:Manning, Christopher D.; Schütze, Hinrich (1999). 2854:Palmer, M.; Babko-Malaya, O.; Dang, H. T. (2004). 2792:Navigli, R.; Litkowski, K.; Hargraves, O. (2007). 2702:Determining word sense dominance using a thesaurus 2562:An information retrieval approach to sense ranking 2531:Gliozzo, A.; Magnini, B.; Strapparava, C. (2004). 1809:Rothe, Sascha; Schütze, Hinrich (September 2017). 1307: 3058:"Word sense disambiguation: The state of the art" 2961:Machine Translation of Languages: Fourteen Essays 1568:"Enriching Word Vectors with Subword Information" 1484: 1439:The Oxford Handbook of Computational Linguistics 1145:"Part-of-speech tagging: Part-of-speech tagging" 2132: 2120: 1967: 1496: 1408: 1283: 391:Semi-supervised or minimally supervised methods 376:There are four conventional approaches to WSD: 289:Sense inventory and algorithms' task-dependency 1372: 3281: 3008:Agirre, Eneko; Edmonds, Philip, eds. (2007). 1943: 1396: 756:Automatic acquisition of sense-tagged corpora 132:data to be disambiguated (in some methods, a 120:Disambiguation requires two strict inputs: a 8: 3239:. New York: Marcel Dekker. pp. 629–654. 2607:Integrating subject field codes into WordNet 2600:(2nd ed.). Oxford: Elsevier Publishers. 1955: 1269:sfn error: no target: CITEREFKilgarrif1997 ( 1204: 928:Word Sense Induction and Disambiguation task 301: 295: 3076:Jurafsky, Daniel; Martin, James H. (2000). 2968:Wilks, Y.; Slator, B.; Guthrie, L. (1996). 3699: 3495: 3288: 3274: 3266: 2548:Sense discrimination with parallel corpora 2522:: CS1 maint: location missing publisher ( 2508:(1997). "Analysis of a handwriting task". 1049: 3262:by Rada Mihalcea and Ted Pedersen (2005). 3121:Kilgarriff, A.; Grefenstette, G. (2003). 2598:Encyclopaedia of Language and Linguistics 2108: 1884: 1866: 1826: 1766: 1726: 1640: 1630: 1593: 1583: 1542: 1517: 1385:Agirre, Lopez de Lacalle & Soroa 2009 1295: 1264: 1131:Association for Computational Linguistics 814:: raw corpora and sense-annotated corpora 3202:Resnik, Philip; Yarowsky, David (2000). 2545:Ide, N.; Erjavec, T.; Tufis, D. (2002). 2184:R. Navigli, D. A. Jurgens, D. Vannella. 1420: 1192: 1105: 1093: 3237:Handbook of Natural Language Processing 3029:Journal of Natural Language Engineering 2644:"The English Lexical Substitution Task" 1908:Gliozzo, Magnini & Strapparava 2004 1472: 1252: 1216: 1067:Navigli, Litkowski & Hargraves 2007 1030: 666:Identification of dominant word senses; 445:Dictionary- and knowledge-based methods 3161:. Cambridge, Massachusetts: MIT Press. 2972:. Cambridge, Massachusetts: MIT Press. 2959:. In Locke, W.N.; Booth, A.D. (eds.). 2575:Building Large Knowledge-Based Systems 2515: 1979: 1037: 3168:"Word Sense Disambiguation: A Survey" 2905:"Automatic word sense discrimination" 2868:Ponzetto, S. P.; Navigli, R. (2010). 1848: 1846: 1804: 1802: 1507: 1505: 199:Most researchers continue to work on 7: 3747:Simple Knowledge Organization System 2734:Navigli, R.; Crisafulli, G. (2010). 1319: 1241:Palmer, Babko-Malaya & Dang 2004 387:, without using any corpus evidence. 50:is the process of identifying which 2206:Multilingual versus monolingual WSD 2146:Multilingual versus monolingual WSD 1143:Martinez, Angel R. (January 2012). 437:have shown superior performance in 3259:Word Sense Disambiguation Tutorial 3056:Ide, Nancy; Véronis, Jean (1998). 2801:. Proc. of Semeval-2007 Workshop ( 2642:McCarthy, D.; Navigli, R. (2009). 2605:Magnini, B.; Cavaglià, G. (2000). 2463:Di Marco, A.; Navigli, R. (2013). 1117:A. Moro; A. Raganato; R. Navigli. 602:of context, a task referred to as 25: 3762:Thesaurus (information retrieval) 2810:Navigli, R.; Velardi, P. (2005). 2651:Language Resources and Evaluation 2498:"Designing a task for SENSEVAL-2" 2348:. Moin.delph-in.net. 2018-02-05. 2164:Els Lefever and Veronique Hoste. 2090:Kilgarrif & Grefenstette 2003 669:WSD using Cross-Lingual Evidence. 3087:"I don't believe in word senses" 3080:. New Jersey, US: Prentice Hall. 2748:Navigli, R.; Lapata, M. (2010). 2699:Mohammad, S.; Hirst, G. (2006). 1855:Expert Systems with Applications 1435:"13.5.3 Two claims about senses" 1308:Wilks, Slator & Guthrie 1996 1014:Sentence boundary disambiguation 978: 186:Differences between dictionaries 2937:Snyder, B.; Palmer, M. (2004). 2573:Lenat, D.; Guha, R. V. (1989). 2559:Lapata, M.; Keller, F. (2007). 2454:Chan, Y. S.; Ng, H. T. (2005). 2378:from the original on 2018-06-11 2352:from the original on 2018-03-09 2327:from the original on 2018-03-12 2302:from the original on 2018-03-21 2298:. Senserelate.sourceforge.net. 2277:from the original on 2018-03-22 2252:from the original on 2014-08-08 2071:Hindi word sense disambiguation 2049:Diab, Mona, and Philip Resnik. 2012:from the original on 2023-07-15 1735:from the original on 2023-01-21 1695:from the original on 2023-01-21 1649:from the original on 2019-10-28 1455:from the original on 2022-02-22 1175:from the original on 2023-07-15 300:– that is, 'financial bank' or 32:Disambiguation (disambiguation) 3343:Natural language understanding 3078:Speech and Language Processing 2440:. Reading, MA: Addison-Wesley. 2346:"Lexical Knowledge Base (LKB)" 2224:Eneko Agirre and Aitor Soroa. 521:, parameter optimization, and 288: 74:, it is usually subconscious. 39:Knowledge (XXG):Disambiguation 1: 3867:Optical character recognition 2926:Learning to Merge Word Senses 1968:Ide, Erjavec & Tufis 2002 1485:Navigli & Crisafulli 2010 1133:(TACL). 2. pp. 231–244. 2014. 893:Classic English WSD uses the 775:Machine-readable dictionaries 718:Local impediments and summary 663:Domain-driven disambiguation; 314:Finally, the very notion of " 3560:Multi-document summarization 3208:Natural Language Engineering 1998:. Massachusetts: MIT Press. 598:word occurrences using some 4042:Natural language processing 3890:Latent Dirichlet allocation 3862:Natural language generation 3727:Machine-readable dictionary 3722:Linguistic Linked Open Data 3297:Natural language processing 2963:. Cambridge, MA: MIT Press. 2678:Mihalcea, R. (April 2007). 2133:Magnini & Cavaglià 2000 2121:Agirre & Stevenson 2007 1992:Shieber, Stuart M. (1992). 1497:Di Marco & Navigli 2013 1409:Ponzetto & Navigli 2010 1284:McCarthy & Navigli 2009 994:Controlled natural language 851:(2004), and its successor, 480:graph connectivity measures 94:supervised machine learning 83:natural language processing 4078: 3642:Explicit semantic analysis 3391:Deep linguistic processing 3142:10.1162/089120103322711569 2940:The English all-words task 2763:(4). IEEE Press: 678–692. 2635:10.1162/coli.2007.33.4.553 1877:10.1016/j.eswa.2019.06.026 1373:Navigli & Velardi 2005 762:External knowledge sources 584: 339:computational applications 36: 29: 4047:Computational linguistics 4037:Word-sense disambiguation 3485:Word-sense disambiguation 3338:Computational linguistics 3220:10.1017/S1351324999002211 3166:Navigli, Roberto (2009). 3130:Computational Linguistics 3065:Computational Linguistics 3041:10.1017/S1351324902002966 2912:Computational Linguistics 2663:10.1007/s10579-009-9084-1 2623:Computational Linguistics 2475:(3). MIT Press: 709–754. 2469:Computational Linguistics 1944:Mohammad & Hirst 2006 1815:Computational Linguistics 1397:Navigli & Lapata 2010 800:Other resources (such as 684:constraint-based grammars 407:word sense discrimination 362:computational linguistics 214:that encodes concepts as 4011:Natural Language Toolkit 3935:Pronunciation assessment 3837:Automatic identification 3667:Latent semantic analysis 3623:Distributional semantics 3508:Compound-term processing 3406:Named-entity recognition 2657:(2). Springer: 139–159. 2438:Language and information 1956:Lapata & Keller 2007 1342:10.1109/CTS.2015.7210442 1205:Snyder & Palmer 2004 544:semi-supervised learning 3915:Automated essay scoring 3885:Document classification 3552:Automatic summarization 3187:10.1145/1459352.1459355 3106:10.1023/A:1000583911091 3085:Kilgarriff, A. (1997). 2952:Weaver, Warren (1949). 2436:Bar-Hillel, Y. (1964). 1433:Mitkov, Ruslan (2004). 1004:Judicial interpretation 880:Classic monolingual WSD 767:classified as follows: 538:Semi-supervised methods 527:Support Vector Machines 473:methods reminiscent of 435:support vector machines 423:Naïve Bayes classifiers 3772:Universal Dependencies 3465:Terminology extraction 3448:Semantic decomposition 3443:Semantic role labeling 3433:Part-of-speech tagging 3401:Information extraction 3386:Coreference resolution 3376:Collocation extraction 2831:10.1109/TPAMI.2005.149 2421:. New York: Springer. 2296:"WordNet::SenseRelate" 1129:. Transactions of the 857:semantic role labeling 808:, domain labels, etc.) 709:. The creation of the 429:. In recent research, 349:Approaches and methods 310:Discreteness of senses 302: 296: 240:part-of-speech tagging 234:Part-of-speech tagging 3533:Sentence segmentation 3175:ACM Computing Surveys 2988:Yarowsky, D. (1995). 2977:Yarowsky, D. (1992). 2769:10.1109/TPAMI.2009.36 2449:. New York: Springer. 2321:"UKB: Graph Base WSD" 2135:, pp. 1413–1418. 1982:, pp. 1037–1042. 1920:Buitelaar et al. 2006 1755:Volume 1: Long Papers 1411:, pp. 1522–1531. 1387:, pp. 1501–1506. 1375:, pp. 1063–1074. 1296:Lenat & Guha 1989 1231:, pp. 1005–1014. 796:Collocation resources 747:information retrieval 741:ever accessible, the 614:knowledge acquisition 600:measure of similarity 592:Unsupervised learning 531:memory-based learning 48:-sense disambiguation 3985:Voice user interface 3696:datasets and corpora 3637:Document-term matrix 3490:Word-sense induction 2903:Schütze, H. (1998). 2713:Navigli, R. (2006). 2506:Fellbaum, Christiane 2496:Edmonds, P. (2000). 2481:10.1162/COLI_a_00148 1932:McCarthy et al. 2007 1828:10.1162/coli_a_00294 1632:10.18653/v1/P16-1085 1595:10.1162/tacl_a_00051 1441:. OUP. p. 257. 1336:. pp. 326–333. 1009:Semantic unification 861:lexical substitution 802:word frequency lists 724:Unsupervised methods 604:word sense induction 587:Word sense induction 581:Unsupervised methods 475:spreading activation 431:kernel-based methods 403:Unsupervised methods 343:lexical substitution 259:Inter-judge variance 62:or other segment of 3965:Interactive fiction 3895:Pachinko allocation 3852:Speech segmentation 3808:Google Ngram Viewer 3580:Machine translation 3570:Text simplification 3565:Sentence extraction 3453:Semantic similarity 2123:, pp. 217–251. 2111:, pp. 753–761. 2092:, pp. 333–347. 1958:, pp. 348–355. 1946:, pp. 121–128. 1934:, pp. 553–590. 1922:, pp. 275–298. 1910:, pp. 380–387. 1785:10.3115/v1/p15-1173 1777:2015arXiv150701127R 1681:10.3115/v1/N15-1132 1544:10.3115/v1/d14-1162 1423:, pp. 189–196. 1399:, pp. 678–692. 1286:, pp. 139–159. 1219:, pp. 105–112. 1096:, pp. 454–460. 1082:Pradhan et al. 2007 1052:, pp. 174–179. 1019:Syntactic ambiguity 871:Task design choices 463:semantic similarity 461:and to compute the 439:supervised learning 263:Another problem is 253:supervised learning 96:methods in which a 68:language processing 3975:Question answering 3847:Speech recognition 3712:Corpus linguistics 3692:Language resources 3475:Textual entailment 3458:Sentiment analysis 2323:. Ixa2.si.ehu.es. 2231:2013-02-28 at the 2211:2012-04-10 at the 2191:2014-08-08 at the 2171:2010-06-16 at the 2151:2012-04-10 at the 2076:2016-03-04 at the 2056:2016-03-04 at the 2036:2016-01-09 at the 1980:Chan & Ng 2005 1475:, pp. 97–123. 1267:, pp. 91–113. 1125:2014-08-08 at the 986:Linguistics portal 751:web search engines 728:Supervised methods 548:Yarowsky algorithm 502:Supervised methods 492:semantic relations 397:Supervised methods 366:Margaret Masterman 238:In any real test, 4057:Lexical semantics 4024: 4023: 3980:Virtual assistant 3905:Computer-assisted 3831: 3830: 3588:Computer-assisted 3546: 3545: 3538:Word segmentation 3500:Text segmentation 3438:Semantic analysis 3426:Syntactic parsing 3411:Ontology learning 2582:Lesk, M. (1986). 2577:. Addison-Wesley. 2512:. Washington D.C. 2005:978-0-262-19324-5 1970:, pp. 54–60. 1448:978-0-19-927634-9 1351:978-1-4673-7647-1 1322:, pp. 24–26. 1243:, pp. 49–56. 1207:, pp. 41–43. 1084:, pp. 87–92. 1069:, pp. 30–35. 910:Cross-lingual WSD 895:Princeton WordNet 702:lexical resources 523:ensemble learning 519:feature selection 226:. More recently, 220:Roget's Thesaurus 175:domain adaptation 16:(Redirected from 4069: 4001:Formal semantics 3950:Natural language 3857:Speech synthesis 3839:and data capture 3742:Semantic network 3717:Lexical resource 3700: 3518:Lexical analysis 3496: 3421:Semantic parsing 3290: 3283: 3276: 3267: 3240: 3231: 3198: 3172: 3162: 3153: 3127: 3117: 3091: 3081: 3072: 3062: 3052: 3023: 2995: 2984: 2973: 2964: 2958: 2948: 2933: 2931: 2919: 2909: 2899: 2897: 2885: 2883: 2876: 2864: 2862: 2850: 2825:(7): 1075–1086. 2816: 2806: 2800: 2788: 2754: 2744: 2742: 2730: 2728: 2721: 2709: 2707: 2695: 2693: 2686: 2674: 2648: 2638: 2620: 2610: 2601: 2592: 2590: 2578: 2569: 2567: 2555: 2553: 2541: 2539: 2527: 2521: 2513: 2501: 2492: 2459: 2450: 2441: 2432: 2413: 2407: 2387: 2386: 2384: 2383: 2367: 2361: 2360: 2358: 2357: 2342: 2336: 2335: 2333: 2332: 2317: 2311: 2310: 2308: 2307: 2292: 2286: 2285: 2283: 2282: 2273:. 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1047: 1041: 1035: 988: 983: 982: 957:semantic network 940:testing data set 917:Multilingual WSD 707:parallel corpora 672:WSD solution in 655:Other approaches 305: 299: 87:machine learning 21: 4077: 4076: 4072: 4071: 4070: 4068: 4067: 4066: 4027: 4026: 4025: 4020: 3989: 3969:Syntax guessing 3951: 3944: 3930:Predictive text 3925:Grammar checker 3906: 3899: 3871: 3838: 3827: 3793:Bank of English 3776: 3704: 3695: 3686: 3617: 3574: 3542: 3494: 3396:Distant reading 3371:Argument mining 3357: 3353:Text processing 3299: 3294: 3248: 3243: 3234: 3201: 3170: 3165: 3156: 3125: 3120: 3089: 3084: 3075: 3060: 3055: 3026: 3020: 3007: 3003: 3001:Further reading 2998: 2987: 2976: 2967: 2956: 2951: 2936: 2929: 2922: 2907: 2902: 2895: 2888: 2881: 2874: 2867: 2860: 2853: 2814: 2809: 2798: 2791: 2752: 2747: 2740: 2733: 2726: 2719: 2712: 2705: 2698: 2691: 2684: 2677: 2646: 2641: 2618: 2613: 2604: 2595: 2588: 2581: 2572: 2565: 2558: 2551: 2544: 2537: 2530: 2514: 2504: 2495: 2462: 2453: 2444: 2435: 2429: 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Github.com. 2362: 2337: 2312: 2287: 2271:"BabelNet API" 2262: 2237: 2217: 2197: 2177: 2157: 2137: 2125: 2113: 2109:Litkowski 2005 2101: 2082: 2062: 2042: 2022: 2004: 1984: 1972: 1960: 1948: 1936: 1924: 1912: 1900: 1886:2027.42/145475 1842: 1821:(3): 593–617. 1798: 1745: 1705: 1659: 1609: 1558: 1525: 1501: 1489: 1477: 1465: 1447: 1425: 1413: 1401: 1389: 1377: 1365: 1350: 1324: 1312: 1300: 1288: 1276: 1265:Kilgarrif 1997 1257: 1245: 1233: 1221: 1209: 1197: 1185: 1155:(1): 107–113. 1135: 1110: 1098: 1086: 1071: 1054: 1042: 1029: 1028: 1026: 1023: 1022: 1021: 1016: 1011: 1006: 1001: 999:Entity linking 996: 990: 989: 973: 970: 969: 968: 965: 962: 959: 953: 948: 945: 944: 943: 925: 914: 907: 906: 905: 898: 872: 869: 820: 817: 816: 815: 809: 798: 791:Unstructured: 789: 788: 783: 778: 763: 760: 743:World Wide Web 719: 716: 715: 714: 691: 688: 687: 686: 680:Type inference 677: 670: 667: 664: 656: 653: 585:Main article: 582: 579: 539: 536: 503: 500: 451:Lesk algorithm 446: 443: 427:decision trees 411: 410: 400: 394: 388: 350: 347: 334:Lexicographers 320:coarse-grained 311: 308: 290: 287: 279:coarse-grained 260: 257: 235: 232: 187: 184: 182: 179: 142: 139: 117: 114: 58:is meant in a 26: 24: 18:Disambiguation 14: 13: 10: 9: 6: 4: 3: 2: 4074: 4063: 4060: 4058: 4055: 4053: 4050: 4048: 4045: 4043: 4040: 4038: 4035: 4034: 4032: 4017: 4014: 4012: 4009: 4007: 4006:Hallucination 4004: 4002: 3999: 3998: 3996: 3992: 3986: 3983: 3981: 3978: 3976: 3973: 3970: 3966: 3963: 3961: 3958: 3957: 3955: 3953: 3947: 3941: 3940:Spell checker 3938: 3936: 3933: 3931: 3928: 3926: 3923: 3921: 3918: 3916: 3913: 3912: 3910: 3908: 3902: 3896: 3893: 3891: 3888: 3886: 3883: 3882: 3880: 3878: 3874: 3868: 3865: 3863: 3860: 3858: 3855: 3853: 3850: 3848: 3845: 3844: 3842: 3840: 3834: 3824: 3821: 3819: 3816: 3814: 3811: 3809: 3806: 3804: 3801: 3799: 3796: 3794: 3791: 3789: 3786: 3785: 3783: 3779: 3773: 3770: 3768: 3765: 3763: 3760: 3758: 3755: 3753: 3752:Speech corpus 3750: 3748: 3745: 3743: 3740: 3738: 3735: 3733: 3732:Parallel text 3730: 3728: 3725: 3723: 3720: 3718: 3715: 3713: 3710: 3709: 3707: 3701: 3698: 3693: 3689: 3683: 3680: 3678: 3675: 3673: 3670: 3668: 3665: 3662: 3658: 3655: 3653: 3650: 3648: 3645: 3643: 3640: 3638: 3635: 3633: 3630: 3629: 3627: 3624: 3620: 3614: 3611: 3609: 3606: 3604: 3601: 3599: 3596: 3594: 3593:Example-based 3591: 3589: 3586: 3585: 3583: 3581: 3577: 3571: 3568: 3566: 3563: 3561: 3558: 3557: 3555: 3553: 3549: 3539: 3536: 3534: 3531: 3529: 3526: 3524: 3523:Text chunking 3521: 3519: 3516: 3514: 3513:Lemmatisation 3511: 3509: 3506: 3505: 3503: 3501: 3497: 3491: 3488: 3486: 3483: 3481: 3478: 3476: 3473: 3471: 3468: 3466: 3463: 3462: 3459: 3456: 3454: 3451: 3449: 3446: 3444: 3441: 3439: 3436: 3434: 3431: 3427: 3424: 3422: 3419: 3418: 3417: 3414: 3412: 3409: 3407: 3404: 3402: 3399: 3397: 3394: 3392: 3389: 3387: 3384: 3382: 3379: 3377: 3374: 3372: 3369: 3368: 3366: 3364: 3363:Text analysis 3360: 3354: 3351: 3349: 3346: 3344: 3341: 3339: 3336: 3332: 3329: 3327: 3324: 3323: 3322: 3319: 3317: 3314: 3312: 3309: 3308: 3306: 3304:General terms 3302: 3298: 3291: 3286: 3284: 3279: 3277: 3272: 3271: 3268: 3261: 3260: 3256: 3253: 3250: 3249: 3245: 3238: 3233: 3229: 3225: 3221: 3217: 3213: 3209: 3205: 3200: 3196: 3192: 3188: 3184: 3180: 3176: 3169: 3164: 3160: 3155: 3151: 3147: 3143: 3139: 3135: 3131: 3124: 3119: 3115: 3111: 3107: 3103: 3100:(2): 91–113. 3099: 3095: 3094:Comput. Human 3088: 3083: 3079: 3074: 3070: 3066: 3059: 3054: 3050: 3046: 3042: 3038: 3034: 3030: 3025: 3021: 3015: 3011: 3006: 3005: 3000: 2993: 2992: 2986: 2982: 2981: 2975: 2971: 2966: 2962: 2955: 2954:"Translation" 2950: 2946: 2942: 2941: 2935: 2928: 2927: 2921: 2917: 2913: 2906: 2901: 2894: 2893: 2887: 2880: 2873: 2872: 2866: 2859: 2858: 2852: 2848: 2844: 2840: 2836: 2832: 2828: 2824: 2820: 2813: 2808: 2804: 2797: 2796: 2790: 2786: 2782: 2778: 2774: 2770: 2766: 2762: 2758: 2751: 2746: 2739: 2738: 2732: 2725: 2718: 2717: 2711: 2704: 2703: 2697: 2690: 2683: 2682: 2676: 2672: 2668: 2664: 2660: 2656: 2652: 2645: 2640: 2636: 2632: 2628: 2624: 2617: 2612: 2608: 2603: 2599: 2594: 2587: 2586: 2580: 2576: 2571: 2564: 2563: 2557: 2550: 2549: 2543: 2536: 2535: 2529: 2525: 2519: 2511: 2507: 2503: 2499: 2494: 2490: 2486: 2482: 2478: 2474: 2470: 2466: 2461: 2457: 2452: 2448: 2443: 2439: 2434: 2430: 2424: 2420: 2415: 2411: 2404: 2399: 2398: 2393: 2377: 2373: 2366: 2363: 2351: 2347: 2341: 2338: 2326: 2322: 2316: 2313: 2301: 2297: 2291: 2288: 2276: 2272: 2266: 2263: 2251: 2247: 2241: 2238: 2234: 2230: 2227: 2221: 2218: 2214: 2210: 2207: 2201: 2198: 2194: 2190: 2187: 2181: 2178: 2174: 2170: 2167: 2161: 2158: 2154: 2150: 2147: 2141: 2138: 2134: 2129: 2126: 2122: 2117: 2114: 2110: 2105: 2102: 2097: 2091: 2086: 2083: 2079: 2075: 2072: 2066: 2063: 2059: 2055: 2052: 2046: 2043: 2039: 2035: 2032: 2026: 2023: 2011: 2007: 2001: 1997: 1996: 1988: 1985: 1981: 1976: 1973: 1969: 1964: 1961: 1957: 1952: 1949: 1945: 1940: 1937: 1933: 1928: 1925: 1921: 1916: 1913: 1909: 1904: 1901: 1896: 1892: 1887: 1882: 1878: 1874: 1869: 1864: 1860: 1856: 1849: 1847: 1843: 1838: 1834: 1829: 1824: 1820: 1816: 1812: 1805: 1803: 1799: 1794: 1790: 1786: 1782: 1778: 1774: 1769: 1764: 1760: 1756: 1749: 1746: 1734: 1729: 1724: 1720: 1716: 1709: 1706: 1694: 1690: 1686: 1682: 1678: 1674: 1670: 1663: 1660: 1648: 1643: 1638: 1633: 1628: 1624: 1620: 1613: 1610: 1605: 1601: 1596: 1591: 1586: 1581: 1577: 1573: 1569: 1562: 1559: 1554: 1550: 1545: 1540: 1536: 1529: 1526: 1520: 1515: 1508: 1506: 1502: 1498: 1493: 1490: 1486: 1481: 1478: 1474: 1469: 1466: 1454: 1450: 1444: 1440: 1436: 1429: 1426: 1422: 1421:Yarowsky 1995 1417: 1414: 1410: 1405: 1402: 1398: 1393: 1390: 1386: 1381: 1378: 1374: 1369: 1366: 1361: 1357: 1353: 1347: 1343: 1339: 1335: 1328: 1325: 1321: 1316: 1313: 1309: 1304: 1301: 1297: 1292: 1289: 1285: 1280: 1277: 1272: 1266: 1261: 1258: 1254: 1249: 1246: 1242: 1237: 1234: 1230: 1225: 1222: 1218: 1213: 1210: 1206: 1201: 1198: 1194: 1193:Fellbaum 1997 1189: 1186: 1174: 1170: 1166: 1162: 1158: 1154: 1150: 1146: 1139: 1136: 1132: 1128: 1124: 1120: 1114: 1111: 1107: 1106:Mihalcea 2007 1102: 1099: 1095: 1094:Yarowsky 1992 1090: 1087: 1083: 1078: 1076: 1072: 1068: 1063: 1061: 1059: 1055: 1051: 1046: 1043: 1039: 1034: 1031: 1024: 1020: 1017: 1015: 1012: 1010: 1007: 1005: 1002: 1000: 997: 995: 992: 991: 987: 981: 976: 971: 966: 963: 960: 958: 954: 951: 950: 946: 941: 937: 934:from a fixed 933: 929: 926: 924:translations. 922: 918: 915: 911: 908: 904: 899: 896: 892: 891: 889: 885: 881: 878: 877: 876: 870: 868: 864: 862: 859:, gloss WSD, 858: 854: 850: 846: 842: 838: 835:(now renamed 834: 829: 827: 818: 813: 810: 807: 803: 799: 797: 794: 793: 792: 787: 784: 782: 779: 776: 773: 772: 771: 768: 761: 759: 757: 752: 748: 744: 740: 735: 733: 729: 725: 717: 712: 711:Hindi WordNet 708: 703: 699: 698: 694: 693: 689: 685: 681: 678: 675: 671: 668: 665: 662: 661: 660: 654: 652: 650: 646: 642: 638: 634: 630: 626: 622: 617: 615: 610: 605: 601: 597: 593: 588: 580: 578: 576: 571: 569: 568:co-occurrence 564: 562: 557: 556:bootstrapping 552: 549: 545: 537: 535: 532: 528: 524: 520: 516: 512: 508: 501: 499: 495: 493: 489: 485: 481: 476: 472: 468: 464: 460: 455: 452: 444: 442: 440: 436: 432: 428: 424: 420: 416: 408: 404: 401: 398: 395: 392: 389: 386: 382: 379: 378: 377: 374: 370: 367: 363: 359: 354: 348: 346: 344: 340: 335: 331: 328: 324: 321: 317: 309: 307: 304: 298: 286: 284: 280: 276: 271: 269: 266: 258: 256: 254: 248: 246: 241: 233: 231: 229: 225: 221: 217: 213: 209: 204: 202: 197: 193: 185: 180: 178: 176: 171: 168: 166: 161: 159: 154: 152: 148: 147:Warren Weaver 140: 138: 135: 131: 127: 123: 115: 113: 111: 106: 104: 99: 95: 90: 88: 84: 80: 75: 73: 69: 65: 61: 57: 53: 49: 47: 40: 33: 19: 3920:Concordancer 3484: 3316:Bag-of-words 3258: 3236: 3211: 3207: 3178: 3174: 3158: 3133: 3129: 3097: 3093: 3077: 3068: 3064: 3032: 3028: 3012:. Springer. 3009: 2990: 2979: 2969: 2960: 2945:the original 2939: 2925: 2918:(1): 97–123. 2915: 2911: 2891: 2879:the original 2870: 2856: 2822: 2818: 2794: 2760: 2756: 2736: 2724:the original 2715: 2701: 2689:the original 2680: 2654: 2650: 2626: 2622: 2606: 2597: 2584: 2574: 2561: 2547: 2533: 2509: 2472: 2468: 2455: 2446: 2437: 2418: 2409: 2380:. Retrieved 2365: 2354:. Retrieved 2340: 2329:. Retrieved 2315: 2304:. Retrieved 2290: 2279:. Retrieved 2265: 2254:. Retrieved 2240: 2220: 2200: 2180: 2160: 2140: 2128: 2116: 2104: 2085: 2065: 2045: 2025: 2014:. Retrieved 1994: 1987: 1975: 1963: 1951: 1939: 1927: 1915: 1903: 1858: 1854: 1818: 1814: 1758: 1754: 1748: 1737:. Retrieved 1718: 1708: 1697:. Retrieved 1672: 1662: 1651:. Retrieved 1642:11573/936571 1622: 1612: 1575: 1571: 1561: 1534: 1528: 1492: 1480: 1473:Schütze 1998 1468: 1457:. Retrieved 1438: 1428: 1416: 1404: 1392: 1380: 1368: 1333: 1327: 1315: 1303: 1291: 1279: 1260: 1253:Edmonds 2000 1248: 1236: 1224: 1217:Navigli 2006 1212: 1200: 1188: 1177:. Retrieved 1152: 1148: 1138: 1113: 1101: 1089: 1045: 1033: 936:training set 874: 865: 830: 822: 790: 770:Structured: 769: 765: 736: 721: 695: 658: 618: 590: 572: 565: 553: 541: 511:common sense 505: 496: 456: 448: 418: 414: 412: 375: 371: 355: 352: 327:fine-grained 313: 292: 283:fine-grained 272: 262: 249: 237: 205: 201:fine-grained 192:dictionaries 189: 181:Difficulties 172: 169: 162: 155: 144: 119: 107: 91: 76: 44: 43: 3877:Topic model 3757:Text corpus 3603:Statistical 3470:Text mining 3311:AI-complete 3181:(2): 1–69. 2394:Works cited 2370:alvations. 2248:. Babelfy. 1861:: 288–303. 1721:: 916–926. 1578:: 135–146. 1038:Weaver 1949 734:exercises. 674:John Ball's 471:Graph-based 459:relatedness 275:upper bound 265:inter-judge 196:thesauruses 66:. In human 4031:Categories 3598:Rule-based 3480:Truecasing 3348:Stop words 3071:(1): 1–40. 2382:2018-03-22 2356:2018-03-22 2331:2018-03-22 2306:2018-03-22 2281:2018-03-22 2256:2018-03-22 2016:2018-12-23 1868:2101.08700 1768:1507.01127 1739:2023-01-21 1728:1707.08084 1699:2023-01-21 1653:2019-10-28 1585:1607.04606 1459:2022-02-22 1179:2021-04-01 1025:References 884:supervised 849:Senseval-3 845:Senseval-2 841:Senseval-1 819:Evaluation 781:Ontologies 700:: Lack of 629:ConceptNet 596:clustering 561:classifier 507:Supervised 482:, such as 381:Dictionary 316:word sense 151:Bar-Hillel 122:dictionary 103:algorithms 98:classifier 4062:Ambiguity 4052:Semantics 3907:reviewing 3705:standards 3703:Types and 2518:cite book 2246:"Babelfy" 1837:0891-2017 1604:2307-387X 1519:1301.3781 1320:Lesk 1986 826:data sets 806:stoplists 575:bilingual 515:reasoning 488:knowledge 323:homograph 243:Senseval/ 110:homograph 105:to date. 72:cognition 3823:Wikidata 3803:FrameNet 3788:BabelNet 3767:Treebank 3737:PropBank 3682:Word2vec 3647:fastText 3528:Stemming 3228:19915022 3049:17866880 2847:12898695 2839:16013755 2777:20224123 2671:16888516 2376:Archived 2350:Archived 2325:Archived 2300:Archived 2275:Archived 2250:Archived 2229:Archived 2209:Archived 2189:Archived 2169:Archived 2149:Archived 2074:Archived 2054:Archived 2034:Archived 2010:Archived 1895:52225306 1793:15687295 1733:Archived 1693:Archived 1689:10778029 1647:Archived 1453:Archived 1360:13260353 1173:Archived 1169:62672734 1123:Archived 972:See also 947:Software 921:BabelNet 847:(2001), 843:(1998), 833:Senseval 786:Thesauri 732:Senseval 633:BabelNet 433:such as 330:polysemy 268:variance 228:BabelNet 130:language 116:Variants 60:sentence 3994:Related 3960:Chatbot 3818:WordNet 3798:DBpedia 3672:Seq2seq 3416:Parsing 3331:Trigram 3150:2649448 3114:3265361 2803:SemEval 2785:1454904 2489:1775181 2372:"pyWSD" 1773:Bibcode 1553:1957433 932:induced 913:corpus. 853:SemEval 837:SemEval 812:Corpora 649:glosses 645:WordNet 641:WordNet 637:synsets 625:WordNet 609:mapping 467:WordNet 245:SemEval 216:synonym 212:lexicon 208:WordNet 141:History 64:context 3967:(c.f. 3625:models 3613:Neural 3326:Bigram 3321:n-gram 3254:(1998) 3226: 3195:461624 3193: 3148: 3112: 3047: 3016: 2845: 2837: 2783: 2775: 2669: 2487: 2425: 2002: 1893: 1835: 1791: 1687: 1602: 1551: 1445: 1358: 1348: 1167: 777:(MRDs) 739:corpus 484:degree 297:banque 126:corpus 4016:spaCy 3661:large 3652:GloVe 3224:S2CID 3191:S2CID 3171:(PDF) 3146:S2CID 3126:(PDF) 3110:S2CID 3090:(PDF) 3061:(PDF) 3045:S2CID 2957:(PDF) 2930:(PDF) 2908:(PDF) 2896:(PDF) 2882:(PDF) 2875:(PDF) 2861:(PDF) 2843:S2CID 2815:(PDF) 2799:(PDF) 2781:S2CID 2753:(PDF) 2741:(PDF) 2727:(PDF) 2720:(PDF) 2706:(PDF) 2692:(PDF) 2685:(PDF) 2667:S2CID 2647:(PDF) 2619:(PDF) 2589:(PDF) 2566:(PDF) 2552:(PDF) 2538:(PDF) 2485:S2CID 2406:(PDF) 1891:S2CID 1863:arXiv 1789:S2CID 1763:arXiv 1723:arXiv 1685:S2CID 1580:arXiv 1549:S2CID 1514:arXiv 1356:S2CID 1165:S2CID 697:Hindi 281:than 203:WSD. 158:Wilks 54:of a 52:sense 3781:Data 3632:BERT 3014:ISBN 2835:PMID 2773:PMID 2524:link 2423:ISBN 2096:help 2000:ISBN 1833:ISSN 1600:ISSN 1443:ISBN 1346:ISBN 1271:help 554:The 529:and 513:and 449:The 425:and 303:rive 222:and 194:and 85:and 70:and 56:word 46:Word 3813:UBY 3216:doi 3183:doi 3138:doi 3102:doi 3037:doi 2827:doi 2765:doi 2659:doi 2631:doi 2477:doi 1881:hdl 1873:doi 1859:136 1823:doi 1781:doi 1677:doi 1637:hdl 1627:doi 1590:doi 1539:doi 1338:doi 1157:doi 682:in 639:in 128:of 4033:: 3222:. 3210:. 3206:. 3189:. 3179:41 3177:. 3173:. 3144:. 3134:29 3132:. 3128:. 3108:. 3098:31 3096:. 3092:. 3069:24 3067:. 3063:. 3043:. 3031:. 2916:24 2914:. 2910:. 2841:. 2833:. 2823:27 2821:. 2817:. 2779:. 2771:. 2761:32 2759:. 2755:. 2665:. 2655:43 2653:. 2649:. 2627:33 2625:. 2621:. 2520:}} 2516:{{ 2483:. 2473:39 2471:. 2467:. 2408:. 2008:. 1889:. 1879:. 1871:. 1857:. 1845:^ 1831:. 1819:43 1817:. 1813:. 1801:^ 1787:. 1779:. 1771:. 1731:. 1717:. 1691:. 1683:. 1671:. 1645:. 1635:. 1621:. 1598:. 1588:. 1574:. 1570:. 1547:. 1504:^ 1451:. 1437:. 1354:. 1344:. 1171:. 1163:. 1151:. 1147:. 1121:. 1074:^ 1057:^ 804:, 758:. 631:, 627:, 525:. 469:. 89:. 3971:) 3694:, 3663:) 3659:( 3289:e 3282:t 3275:v 3230:. 3218:: 3212:5 3197:. 3185:: 3152:. 3140:: 3116:. 3104:: 3051:. 3039:: 3033:8 3022:. 2849:. 2829:: 2787:. 2767:: 2673:. 2661:: 2637:. 2633:: 2526:) 2491:. 2479:: 2431:. 2412:. 2385:. 2359:. 2334:. 2309:. 2284:. 2259:. 2098:) 2019:. 1897:. 1883:: 1875:: 1865:: 1839:. 1825:: 1795:. 1783:: 1775:: 1765:: 1742:. 1725:: 1702:. 1679:: 1656:. 1639:: 1629:: 1606:. 1592:: 1582:: 1576:5 1555:. 1541:: 1522:. 1516:: 1499:. 1487:. 1462:. 1362:. 1340:: 1310:. 1298:. 1273:) 1255:. 1195:. 1182:. 1159:: 1153:4 1108:. 1040:. 942:. 886:/ 419:n 415:n 409:. 41:. 34:. 20:)

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