Name
Abstract | ||
|---|---|---|
Adjectives are words that describe or modify other elements in a sentence. As such, they are frequently used to convey facts and opinions about the nouns they modify. Connecting nouns to the corresponding adjectives becomes vital for intelligent tasks such as aspect-level sentiment analysis or interpretation of complex queries e.g., \"<em>small hotel with large rooms</em>\" for fine-grained information retrieval. To respond to the need, we propose a methodology that identifies dependencies of nouns and adjectives by looking at syntactic clues related to part-of-speech sequences that help recognize such relationships. These sequences are generalized into patterns that are used to train a binary classifier using machine learning methods. The capabilities of the new method are demonstrated in two, syntactically different languages: English, the leading language of international discourse, and Hebrew, whose rich morphology poses additional challenges for parsing. In each language we compare our method with a designated, state-of-the-art parser and show that it performs similarly in terms of accuracy while: a our method uses a simple and relatively small training set; b it does not require a language specific adaptation, and c it is robust across a variety of writing styles. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-642-54906-9_22 | CICLing |
Keywords | Field | DocType |
information retrieval,parsing,relation extraction | Binary classification,Sentiment analysis,Computer science,Writing style,Noun,Artificial intelligence,Natural language processing,Parsing,Sentence,Syntax,Relationship extraction | Conference |
Volume | ISSN | Citations |
8403 | 0302-9743 | 7 |
PageRank | References | Authors |
0.44 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nir Ofek | 1 | 80 | 7.69 |
| Lior Rokach | 2 | 2127 | 142.59 |
| Prasenjit Mitra | 3 | 2439 | 167.89 |