Taking antonymy mask off in vector space

Automatic detection of antonymy is an important task in Natural Language Processing (NLP) for Information Retrieval (IR), Ontology Learning (OL) and many other semantic applications. However, current unsupervised approaches to antonymy detection are still not fully effective because they cannot discriminate antonyms from synonyms. In this paper, we introduce APAnt, a new Average-Precision-based measure for the unsupervised discrimination of antonymy from synonymy 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 across different parts of speech, including nouns, adjectives and verbs. APAnt outperforms the vector cosine and a baseline model implementing the co-occurrence hypothesis

Antonym-Synonym Classification Based on New Sub-space Embeddings

Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed from the distilled sub-spaces along with some word level features to distinguish antonyms from synonyms. Experimental results show that the proposed model outperforms existing research on antonym synonym distinction in both speed and performance

Lexical Opposition in Discourse Contrast

We investigate the connection between lexical opposition and discourse relations, with a focus on the relation of contrast, in order to evaluate whether opposition participates in discourse relations. Through a corpus-based analysis of Italian documents, we show that the relation between opposition and contrast is not crucial, although not insignificant in the case of implicit relation. The correlation is even weaker when other discourse relations are taken into account.Studiamo la connessione tra l’opposizione lessicale e le relazioni del discorso, con attenzione alla relazione di contrasto, per verificare se l’opposizione partecipa alle relazioni del discorso. Attraverso un’analisi basata su un corpus di documenti in italiano, mostriamo che la relazione tra opposizione e contrasto non è cruciale, anche se non priva di importanza soprattutto per i casi di contrasto implicito. La correlazione sembra più debole se consideriamo le altre relazioni del discorso

Lexical Opposition in Discourse Contrast

We investigate the connection between lexical opposition and discourse relations, with a focus on the relation of contrast, in order to evaluate whether opposition participates in discourse relations. Through a corpus-based analysis of Italian documents, we show that the relation between opposition and contrast is not crucial, although not insignificant in the case of implicit relation. The correlation is even weaker when other discourse relations are taken into account.Studiamo la connessione tra l’opposizione lessicale e le relazioni del discorso, con attenzione alla relazione di contrasto, per verificare se l’opposizione partecipa alle relazioni del discorso. Attraverso un’analisi basata su un corpus di documenti in italiano, mostriamo che la relazione tra opposizione e contrasto non è cruciale, anche se non priva di importanza soprattutto per i casi di contrasto implicito. La correlazione sembra più debole se consideriamo le altre relazioni del discorso

When Similarity Becomes Opposition: Synonyms and Antonyms Discrimination in DSMs

This paper analyzes the concept of opposition and describes a fully unsupervised method for its automatic discrimination from near-synonymy in Distributional Semantic Models (DSMs). The discriminating method is based on the hypothesis that, even though both near-synonyms and opposites are mostly distributionally similar, opposites are different from each other in at least one dimension of meaning, which can be assumed to be salient. Such hypothesis has been implemented in APAnt, a distributional measure that evaluates the extent of the intersection among the most relevant contexts of two words (where relevance is measured as mutual dependency), and its saliency (i.e. their average rank in the mutual dependency sorted list of contexts). The measure – previously introduced in some pilot studies – is presented here with two variants. Evaluation shows that it outperforms three baselines in an antonym retrieval task: the vector cosine, a baseline implementing the co-occurrence hypothesis, and a random rank. This paper describes the algorithm in details and analyzes its current limitations, suggesting that extensions may be developed for discriminating antonyms not only from near-synonyms but also from other semantic relations. During the evaluation, we have noticed that APAnt also has a particular preference for hypernyms

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges