Word Sense Detection (WSD) Algorithm
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A word sense detection (WSD) algorithm is a multi-class shallow semantic NLP algorithm that can be implemented by a WSD system to solve a WSD task.
- Context:
- It can range from being a Heuristic WSD Algorithm to being a Data-Driven WSD Algorithm (such as a supervised WSD algorithm or an unsupervised WSD algorithm).
- It can be supported by a Tokenization Algorithm.
- It can iterate through each Word Mention.
- It can be applied by a Word Sense Disambiguation System.
- It can make use of a Lexical Semantic Similarity Function.
- It can make use of the Word Mention Context Window around the Word Mention.
- ...
- Example(s):
- Counter-Example(s):
- See: Word Sense, Word Sense Disambiguation, Natural Language Processing, Natural Language Understanding, Language Model, Morphological Analysis System.
References
2020
- (Bevilacqua & Navigli, 2020) ⇒ Michele Bevilacqua, and Roberto Navigli. (2020). “Breaking through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information.” In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
- QUOTE: ... We present Enhanced WSD Integrating Synset Embeddings and Relations (EWISER), a neural supervised architecture that is able to tap into this wealth of knowledge by embedding information from the LKB graph within the neural architecture, and to exploit pretrained synset embeddings, enabling the network to predict synsets that are not in the training set. As a result, we set a new state of the art on almost all the evaluation settings considered, also breaking through, for the first time, the 80% ceiling on the concatenation of all the standard all-words English WSD evaluation benchmarks. ...
- Presentation: https://slideslive.com/38929222/breaking-through-the-80-glass-ceiling-raising-the-state-of-the-art-in-word-sense-disambiguation-by-incorporating-knowledge-graph-information
2019
- (Hadiwinoto et al., 2019) ⇒ Christian Hadiwinoto, Hwee Tou Ng, and Wee Chung Gan. (2019). “Improved Word Sense Disambiguation Using Pre-trained Contextualized Word Representations.” In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).
2009
- (Sinha & Mihalcea, 2009) ⇒ Ravi Sinha, and Rada Mihalcea. (2009). “Unsupervised Graph-based Word Sense Disambiguation.” In: “Current Issues in Linguistic Theory: Recent Advances in Natural Language Processing”, Editors Nicolas Nicolov and Ruslan Mitkov. John Benjamins Publishers. ISBN:1556195915
2007
- (Mihalcea, 2007) ⇒ Rada Mihalcea. (2007). “Using Wikipedia for Automatic Word Sense Disambiguation.” In: Proceedings of NAACL-HLT, 2007
- This paper describes a method for generating sense-tagged data using Wikipedia as a source of sense annotations. Through word sense disambiguation experiments, we show that the Wikipedia-based sense annotations are reliable and can be used to construct accurate sense classifiers."
2006a
- (Bunescu & Pasca, 2006) ⇒ Razvan C. Bunescu and Marius Paşca. (2006). “Using encyclopedic knowledge for named entity disambiguation.” In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-06).
2006b
- (AgirreMLS) ⇒ Eneko Agirre, David Martínez, Oier López de Lacalle and Aitor Soroa. (2006). “Two graph-based algorithms for state-of-the-art WSD.” In: Proceedings of EMNLP06 at ACL06.
- This paper explores the use of two graph algorithms for unsupervised induction and tagging of nominal word senses based on corpora.
2005
- (Mihalcea & Petersen, 2005) ⇒ Rada Mihalcea, and Ted Pedersen. (2005). “Advances in Word Sense Disambiguation." Tutorial at ACL-2005.
2004
- (Pedersen et al., 2004) ⇒ Ted Pedersen, Siddharth Patwardhan, and Jason Michelizzi. (2004). “WordNet::Similarity - Measuring the Relatedness of Concepts.” In: Proceedings of the Nineteenth National Conference on Artificial Intelligence - Intelligent Systems Demonstration (AAAI-04).
2003a
- (Galley and McKeown, 2003) ⇒ Michel Galley, and Kathleen R. McKeown. (2003). “Improving Word Sense Disambiguation in Lexical Chaining.” In: Proceedings of IJCAI 2003.
2003b
- (Patwardhan et al., 2003) ⇒ (2003). Siddharth Patwardhan, Satanjeev Banerjee, and Ted Pedersen. “Using Measures of Semantic Relatedness for Word Sense Disambiguation.” In: Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2003).
2002
- (Lee & Ng, 2002) ⇒ Yoong Keok Lee, and Hwee Tou Ng. (2002). “An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation.” In: Proceedings of EMNLP 2002. doi:10.3115/1118693.1118699
- ABSTRACT: In this paper, we evaluate a variety of knowledge sources and supervised learning algorithms for word sense disambiguation on SENSEVAL-2 and SENSEVAL-1 data. Our knowledge sources include the part-of-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic relations. The learning algorithms evaluated include Support Vector Machines (SVM), Naive Bayes, AdaBoost, and decision tree algorithms. We present empirical results showing the relative contribution of the component knowledge sources and the different learning algorithms. In particular, using all of these knowledge sources and SVM (i.e., a single learning algorithm) achieves accuracy higher than the best official scores on both SENSEVAL-2 and SENSEVAL-1 test data.
1998a
- (Leacock et al., 1998) ⇒ Claudia Leacock, Martin Chodorow, and George A. Miller, (1998). “Using Corpus Statistics and WordNet Relations for Sense Identification.” In: Computational Linguistics, 24(1):147–165
- It presents a method to obtain sense-tagged examples using monosemous relatives.
1998b
- (Leacock & Chodorow, 1998) ⇒ Claudia Leacock, and Martin Chodorow. (1998). “Combining local context with WordNet Similarity for Word Sense Identification].” In: Christiane Fellbaum, editor, WordNet: A Lexical Reference System and its Application. MIT Press, Cambridge, MA.
1998c
- (Pedersen & Bruce, 1998) ⇒ Ted Pedersen, and Rebecca Bruce. (1998). “Knowledge lean word-sense disambiguation.” In: Proceedings of the 15th National Conference on Artificial Intelligence.
1997
- (Jiang and Conrath) ⇒ Jay J. Jiang, and David W. Conrath. (1997). “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy.” In: Proceedings on International Conference on Research in Computational Linguistics.
1996a
- (Agirre & Rigau, 1996) ⇒ Eneko Agirre, and German Rigau. (1996). “Word Sense Disambiguation Using Conceptual Density.” In: Proceedings of the 16th conference on Computational Linguistics. doi:10.3115/992628.992635
1996b
- (Ng and Lee, 1996) ⇒ Hwee Tou Ng, and Hian Beng Lee. (1996). “Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-based Approach.” In: Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics (ACL 1996).
1995
- (Yarowsky, 1995) ⇒ David Yarowsky. (1995). “Unsupervised Word Sense Disambiguation Rivaling Supervised Methods." Proceedings of the 33rd annual meeting on Association for Computational Linguistics. http://dx.doi.org/10.3115/981658.981684
1993
- (Leacock et al., 1993) ⇒ Claudia Leacock, Geoffrey Towell, and Ellen Voorhees. (1993). “Corpus-based Statistical Sense Resolution.” In: Proceedings of the workshop on Human Language Technology at HLT 1993.
1986
- (Lesk, 1986) ⇒ Michael Lesk. (1986). “Automatic Sense Disambiguation Uusing Machine Readable Dictionaries: How to tell a pine cone from a ice cream cone.” In: Proceedings of SIGDOC-1986.