Name
Abstract | ||
|---|---|---|
In this article, we propose to evaluate the lexical similarity information provided by word representations against several opinion resources using traditional Information Retrieval tools. Word representation have been used to build and to extend opinion resources such as lexicon, and ontology and their performance have been evaluated on sentiment analysis tasks. We question this method by measuring the correlation between the sentiment proximity provided by opinion resources and the semantic similarity provided by word representations using different correlation coefficients. We also compare the neighbors found in word representations and list of similar opinion words. Our results show that the proximity of words in state-of-the-art word representations is not very effective to build sentiment similarity. |
Year | Venue | Keywords |
|---|---|---|
2016 | LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | opinion lexicon evaluation,sentiment analysis,spectral representation,word embedding |
Field | DocType | Citations |
Semantic similarity,Ontology,Lexical similarity,Word representation,Information retrieval,Sentiment analysis,Computer science,Lexicon,Natural language processing,Artificial intelligence,Word embedding,Spectral representation | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Grégoire Jadi | 1 | 0 | 0.34 |
| Vincent Claveau | 2 | 162 | 32.15 |
| Béatrice Daille | 3 | 306 | 34.40 |
| Laura Monceaux | 4 | 51 | 9.99 |