Enrico Santus

Chasing Hypernyms in Vector Spaces with Entropy

In this paper, we introduce SLQS , a new entropy-based measure for the unsupervised identification of hypernymy and its directionality in Distributional Semantic Models (DSMs). SLQS is assessed through two tasks: (i.) identifying the hypernym in hyponym-hypernym pairs, and (ii.) discriminating hypernymy among various semantic relations. In both tasks, SLQS outperforms other state-of-the-art measures.

EVALution 1.0: an Evolving Semantic Dataset for Training and Evaluation of Distributional Semantic Models

In this paper, we introduce EVALution 1.0, a dataset designed for the training and the evaluation of Distributional Semantic Models (DSMs). This version consists of almost 7.5K tuples, instantiating several semantic relations between word pairs (including hypernymy, synonymy, antonymy, meronymy). The dataset is enriched with a large amount of additional information (i.e. relation domain, word frequency, word POS, word semantic field, etc.) that can be used for either filtering the pairs or performing an in-depth analysis of the results. The tuples were extracted from a combination of ConceptNet 5.0 and WordNet 4.0, and subsequently filtered through automatic methods and crowdsourcing in order to ensure their quality. The dataset is freely downloadable1. An extension in RDF format, including also scripts for data processing, is under development.

LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets

In this paper, we describe the system we built for Task 11 of SemEval2015, which aims at identifying the sentiment intensity of figurative language in tweets. We use various features, including those specially concerned with the identification of irony and sarcasm. The features are evaluated through a decision tree regression model and a support vector regression model. The experiment result of the fivecross validation on the training data shows that the tree regression model outperforms the support vector regression model. The former is therefore used for the final evaluation of the task. The results show that our model performs especially well in predicting the sentiment intensity of tweets involving irony and sarcasm.

Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity

Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, thereby loosing important information for word expectations, such as word interrelations. In this paper, we present a DSM that addresses this issue by defining verb contexts as joint syntactic dependencies. We test our representation in a verb similarity task on two datasets, showing that joint contexts achieve performances comparable to single dependencies or even better. Moreover, they are able to overcome the data sparsity problem of joint feature spaces, in spite of the limited size of our training corpus.

German in Flux: Detecting Metaphoric Change via Word Entropy

This paper explores the information-theoretic measure entropy to detect metaphoric change, transferring ideas from hypernym detection to research on language change. We also build the first diachronic test set for German as a standard for metaphoric change annotation. Our model shows high performance, is unsupervised, language-independent and generalizable to other processes of semantic change.