Semantic Word Similarity System
(Redirected from Semantic Word Similarity Modelling System)
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A Semantic Word Similarity System is a semantic similarity system that can solve a semantic word similarity task (that measures the similarity distance between words).
- AKA: Semantic Word Similarity Machine Learning System, Semantic Word Similarity Analysis System, Semantic Word Similarity Modelling System.
- Context:
- It can solve a Semantic Word Similarity Task by implementing a Semantic Word Similarity Algorithm.
- It can range from being a Monolingual Semantic Word Similarity System, to being a Multilingual Semantic Word Similarity Analysis System, to being a Cross-lingual Semantic Word System.
- It can be evaluated by a Semantic Word Similarity Benchmark Task.
- Example(s):
- Counter-Example(s):
- See: SemEval 2017 Task 2, Word Embedding Task, Lexical Similarity Function, Semantic Textual Similarity Task, Natural Language Processing, Language Model.
References
2021
- (Chandrasekaran & Mago, 2021) ⇒ Dhivya Chandrasekaran, and Vijay Mago. (2021). “Evolution of Semantic Similarity - A Survey.” In: ACM Computing Surveys, 54(2).
- QUOTE: Semantic similarity methods usually give a ranking or percentage of similarity between texts, rather than a binary decision as similar or not similar. Semantic similarity is often used synonymously with semantic relatedness. However, semantic relatedness not only accounts for the semantic similarity between texts but also considers a broader perspective analyzing the shared semantic properties of two words. For example, the words ‘
coffee’ and ‘mug’ may be related to one another closely, but they are not considered semantically similar whereas the words ‘coffee’ and ‘tea’ are semantically similar. Thus, semantic similarity may be considered, as one of the aspects of semantic relatedness. The semantic relationship including similarity is measured in terms of semantic distance, which is inversely proportional to the relationship (...)
- QUOTE: Semantic similarity methods usually give a ranking or percentage of similarity between texts, rather than a binary decision as similar or not similar. Semantic similarity is often used synonymously with semantic relatedness. However, semantic relatedness not only accounts for the semantic similarity between texts but also considers a broader perspective analyzing the shared semantic properties of two words. For example, the words ‘
2017
- (SemEval, 2017) ⇒ SemEval-2017 Task 2: https://alt.qcri.org/semeval2017/task2/
- QUOTE: Semantic similarity is a core field of Natural Language Processing (NLP) which deals with measuring the extent to which two linguistic items are similar. In particular, the word semantic similarity framework is widely accepted as the most direct in-vitro evaluation of semantic vector space models (e.g., word embeddings) and in general semantic representation techniques. As a result, word similarity datasets play a major role in the advancement of research in lexical semantics. Given the importance of moving beyond the barriers of English language by developing language-independent techniques, the SemEval-2017 Task 2 provides a reliable framework for evaluating both monolingual and multilingual semantic representations, and similarity techniques.
2014
- (Pennington et al., 2014) ⇒ Jeffrey Pennington, Richard Socher, and Christopher D. Manning. (2014). “GloVe: Global Vectors for Word Representation.” In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014).