Synonym Extraction Task
(Redirected from Synonym Recognition Task)
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A Synonym Extraction Task is a semantically related words extraction task that requires the creation of synonym sets.
- AKA: Synonym Recognition, Synset Identification.
- Context:
- Output: A set of Synonym Sets.
- It can range from (typically) being a Synonymous Nouns Extraction Task to being a Synonymous Verbs Extraction Task, to being a Synonymous Adjectives Extraction Task, to being a Synonymous Adverbs Extraction Task, depending on the part-of-speech role.
- It can range from being an Open Synonym Extraction Task to being a Closed Synonym Extraction Task (with a lexicon).
- It can range from being an Heuristic Synonym Extraction Task to being a Data-Driven Synonym Extraction Task, such as Unsupervised Synonym Extraction to being a Supervised Synonym Extraction.
- It can range from being a Domain-Specific Synonym Extraction Task to being a Synonymous Common Usage Word Extraction Task.
- It can be solved by a Synonym Extraction System (that implements a Synonym Extraction Algorithm)
- Example(s):
- Counter-Example(s):
- See: Semantic Similarity Measure, Word Sense Relation.
References
2012
- (Wang & Hirst, 2012) ⇒ Tong Wang, and Graeme Hirst. (2012). “Exploring Patterns in Dictionary Definitions for Synonym Extraction.” In: Natural Language Engineering, 18(03).
- ABSTRACT: Automatic determination of synonyms and/or semantically related words has various applications in Natural Language Processing. Two mainstream paradigms to date, lexicon-based and distributional approaches, both exhibit pros and cons with regard to coverage, complexity, and quality. In this paper, we propose three novel methods — two rule-based methods and one machine learning approach — to identify synonyms from definition texts in a machine-readable dictionary. Extracted synonyms are evaluated in two extrinsic experiments and one intrinsic experiment. Evaluation results show that our pattern-based approach achieves best performance in one of the experiments and satisfactory results in the other, comparable to corpus-based state-of-the-art results.
2006
- (Fang et al., 2006) ⇒ Haw-ren Fang, Kevin P. Murphy, Yang Jin, Jessica S. Kim, and Peter S. White. (2006). “Human Gene Name Normalization Using Text Matching with Automatically Extracted Synonym Dictionaries.” In: Proceedings of the BioNLP Workshop on Linking Natural Language Processing and Diology (BioNLP 2006).
- QUOTE: The system identifies human gene synonyms from online databases to generate an extensive synonym lexicon. The lexicon is then compared to a list of candidate gene mentions using various string transformations that can be applied and chained in a flexible order, followed by exact string matching or approximate string matching.
2001
- (Turney, 2001) ⇒ Peter D. Turney. (2001). “Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL.” In: Proceedings of the 12th European Conference on Machine Learning (ECML 2001). doi:10.1007/3-540-44795-4_42
- QUOTE: This paper introduces a simple unsupervised learning algorithm for recognizing synonyms. The task of recognizing synonyms is, given a problem word and a set of alternative words, choose the member from the set of alternative words that is most similar in meaning to the problem word.
- (Hamon & Nazarenko, 2001) ⇒ Thierry Hamon et Adeline Nazarenko. (2001). “Detection of Synonymy Links between Terms: experiment and results.” In: (Bourigault et al., 2001), Pages 185-208.