Good, Great, Excellent: Global Inference of Lexical Intensities
Title | Good, Great, Excellent: Global Inference of Lexical Intensities |
Publication Type | Conference Paper |
Year of Publication | 2013 |
Authors | de Melo, G., & Bansal M. |
Other Numbers | 3893 |
Abstract | Adjectives like good, great, and excellent aresimilar in meaning, but differ in intensity. Intensityorder information is very useful forlanguage learners as well as in several NLPtasks, but is missing in most lexical resources(dictionaries, WordNet, and thesauri). In thispaper, we present a primarily unsupervisedapproach that uses semantics from Web-scaledata (e.g., phrases like good but not excellent)to rank words by assigning them positionson a continuous scale. We rely on MixedInteger Linear Programming to jointly determinethe ranks, such that individual decisionsbenefit from global information. When rankingEnglish adjectives, our global algorithmachieves substantial improvements over previouswork on both pairwise and rank correlation |
Acknowledgment | This work was partially funded by the Deutscher Akademischer Austausch Dienst (DAAD) through a postdoctoral fellowship. |
URL | https://www.icsi.berkeley.edu/pubs/ai/ICSI_goodgreatexcellent13.pdf |
Bibliographic Notes | Annual Meeting of the Association for Computational Linguistics (ACL 2013), Sofia, Bulgaria, August 2013 |
Abbreviated Authors | G. de Melo and M. Bansal |
ICSI Research Group | AI |
ICSI Publication Type | Article in conference proceedings |