Name
Abstract | ||
|---|---|---|
Automatic extraction of synonymous collocation pairs from text corpora is a challenging task of NLP. In order to search collocations of similar meaning in English texts, we use logical-algebraic equations. These equations combine grammatical and semantic characteristics of words of substantive, attributive and verbal collocations types. With Stanford POS tagger and Stanford Universal Dependencies parser, we identify the grammatical characteristics of words. We exploit WordNet synsets to pick synonymous words of collocations. The potential synonymous word combinations found are checked for compliance with grammatical and semantic characteristics of the proposed logical-linguistic equations. Our dataset includes more than half a million Wikipedia articles from a few portals. The experiment shows that the more frequent synonymous collocations occur in texts, the more related topics of the texts might he. The precision of synonymous collocations search in our experiment has achieved the results close to other studies like ours. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.15439/2018F186 | Federated Conference on Computer Science and Information Systems |
Field | DocType | ISSN |
Attributive,Task analysis,Computer science,Text corpus,Encyclopedia,Natural language processing,Artificial intelligence,Parsing,WordNet,Machine learning,Semantics,Collocation | Conference | 2325-0348 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nina Khairova | 1 | 6 | 1.82 |
| Svitlana Petrasova | 2 | 0 | 0.34 |
| Wlodzimierz Lewoniewski | 3 | 30 | 2.80 |
| Mamyrbayev Orken | 4 | 0 | 0.34 |
| Mukhsina Kuralai | 5 | 0 | 0.68 |