A Semantic and Syntactic Similarity Measure for Political Tweets
| Autor: | David McLean, Claire Little, Keeley Crockett, Bruce Edmonds |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
word embeddings
Word embedding General Computer Science Computer science Context (language use) 02 engineering and technology Similarity measure computer.software_genre Semantic similarity Semantic equivalence 020204 information systems Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Selection (linguistics) General Materials Science business.industry General Engineering similarity measure 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business computer lcsh:TK1-9971 Utterance Natural language processing |
| Zdroj: | IEEE Access, Vol 8, Pp 154095-154113 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3017797 |
| Popis: | Measurement of the semantic and syntactic similarity of human utterances is essential in allowing machines to understand dialogue with users. However, human language is complex, and the semantic meaning of an utterance is usually dependent upon the context at a given time and learnt experience of the meaning of the words that are used. This is particularly challenging when automatically understanding the meaning of social media, such as tweets, which can contain non-standard language. Short Text Semantic Similarity measures can be adapted to measure the degree of similarity of a pair of tweets. This work presents a new Semantic and Syntactic Similarity Measure (TSSSM) for political tweets. The approach uses word embeddings to determine semantic similarity and extracts syntactic features to overcome the limitations of current measures which may miss identical sequences of words. A large dataset of tweets focusing on the political domain were collected, pre-processed and used to train the word embedding model, with various experiments performed to determine the optimal model and parameters. A selection of tweet pairs were evaluated by humans for semantic equivalence and correlated against the measure. The new measure can be used in a variety of applications, including for identifying and analyzing political narratives. Experiments on three diverse human-labelled test datasets demonstrate that the measure outperforms an existing measure, performs well on tweets from the political domain and may also generalize outside the political domain. |
| Databáze: | OpenAIRE |
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