Unsupervised Antonym-Synonym Discrimination in Vector Space
Authors: Enrico Santus, Qin Lu, Alessandro Lenci, Chu-Ren Huang (The Hong Kong Polytechnic University Hong Kong, Università di Pisa, 2014)
Topic: Distributional Semantics

Abstract
Automatic detection of antonymy is an important task in Natural Language Processing (NLP). However, currently, there is no effective measure to discriminate antonyms from synonyms because they share many common features. In this paper, we introduce APAnt, a new Average-Precision-based measure for the unsupervised identification of antonymy using Distributional Semantic Models (DSMs). APAnt makes use of Average Precision to estimate the extent and salience of the intersection among the most descriptive contexts of two target words. Evaluation shows that the proposed method is able to distinguish antonyms and synonyms with high accuracy, outperforming a baseline model implementing the co-occurrence hypothesis.

Introduction
Antonymy is one of the fundamental relations shaping the organization of the semantic lexicon and its identification is very challenging for computational models (Mohammad et al., 2008). Yet, antonymy is essential for many Natural Language Processing (NLP) applications, such as Machine Translation (MT), Sentiment Analysis (SA) and Information Retrieval (IR) (Roth and Schulte im Walde, 2014; Mohammad et al., 2013). As well as for other semantic relations, computational lexicons and thesauri explicitly encoding antonymy already exist. Although such resources are often used to support the above mentioned NLP tasks, they have low coverage and many scholars have shown their limits: Mohammad et al. (2013), for example, have noticed that “more than 90% of the contrasting pairs in GRE closest-to-opposite questions are not listed as opposites in WordNet”. The automatic identification of semantic relations is a core task in computational semantics. Distributional Semantic Models (DSMs) have often been used for their well known ability to identify semantically similar lexemes using corpus-derived co-occurrences encoded as distributional vectors (Santus et al., 2014a; Baroni and Lenci, 2010; Turney and Pantel, 2010; Padó and Lapata, 2007; Sahlgren, 2006). These models are based on the Distributional Hypothesis (Harris, 1954) and represent lexical semantic similarity in function of distributional similarity, which can be measured by vector cosine (Turney and Pantel, 2010). However, these models are characterized by a major shortcoming. That is, they are not able to discriminate among different kinds of semantic relations linking distributionally similar lexemes. For instance, the nearest neighbors of castle in the vector space typically include hypernyms like building, co-hyponyms like house, meronyms like brick, antonyms like shack, together with other semantically related words. While impressive results have been achieved in the automatic identification of synonymy (Baroni and Lenci, 2010; Padó and Lapata, 2007), methods for the identification of hypernymy (Santus et al., 2014a; Lenci and Benotto, 2012) and antonymy (Roth and Schulte im Walde, 2014; Mohammad et al., 2013) still need much work to achieve satisfying precision and coverage (Turney, 2008; Mohammad et al., 2008). This is the reason why semisupervised pattern-based approaches have often been preferred to purely unsupervised DSMs (Pantel and Pennacchiotti, 2006; Hearst, 1992) In this paper, we introduce a new Average- Precision-based distributional measures that is able to successfully discriminate antonyms from synonyms, outperforming a baseline implementing the co-occurrence hypothesis, formulated by Charles and Miller in 1989 and confirmed in other studies, such as those of Justeson and Katz (1991) and Fellbaum (1995).

Conclusion
This paper introduces APAnt, a new distributional measure for the identification of antonymy (an extended version of this paper will appear in Santus et al., 2014b). APAnt is evaluated in a discrimination task in which both antonymy- and synonymy-related pairs are present. In the task, APAnt has outperformed the baseline implementing the co-occurrence hypothesis (Fellbaum, 1995; Justeson and Katz, 1991; Charles and Miller, 1989) by 17%. APAnt performance supports our hypothesis, according to which synonyms share a number of salient contexts that is significantly higher than the one shared by antonyms. Ongoing research includes the application of APAnt to discriminate antonymy also from other semantic relations and to automatically extract antonymy-related pairs for the population of ontologies and lexical resources. Further work can be conducted to apply APAnt to other languages.