This paper addresses the problem of acquiring lexical semantic relationships, applied to the lexical entailment relation. Our main contribution is a novel conceptual integration between the two distinct acquisition paradigms for lexical relations – the patternbased and the distributional similarity approaches. The integrated method exploits mutual complementary information of the two approaches...

The lack of large-scale, freely available and durable lexical resources, and the consequences for NLP, is widely acknowledged but the attempts to cope with usual bottlenecks preventing their development often result in dead-ends. This article introduces a language-independent, semi-automatic and endogenous method for enriching lexical resources, based on collaborative editing and random walks t...

A central topic in natural language processing is the design of lexical and syntactic features suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical similarities. We define efficient and powerful kernels for measuring the similarity between dependency structures, whose surf...

We present a new hybrid lexical knowledge base that combines the contextual information of distributional models with the conciseness and precision of manually constructed lexical networks. The computation of our countbased distributional model includes the induction of word senses for single-word and multi-word terms, the disambiguation of word similarity lists, taxonomic relations extracted b...

This paper designs a novel lexical hub to disambiguate word sense, using both syntagmatic and paradigmatic relations of words. It only employs the semantic network of WordNet to calculate word similarity, and the Edinburgh Association Thesaurus (EAT) to transform contextual space for computing syntagmatic and other domain relations with the target word. Without any back-off policy the result on...

We propose two improvements on lexical association used in embedding learning: factorizing individual dependency relations and using lexicographic knowledge from monolingual dictionaries. Both proposals provide low-entropy lexical cooccurrence information, and are empirically shown to improve embedding learning by performing notably better than several popular embedding models in similarity tas...

In a lexicalized grammar formalism such as Lexicalized Tree-Adjoining Grammar (LTAG), each lexical item is associated with at least one elementary structure (supertag) that localizes syntactic and semantic dependencies. Thus a parser for a lexicalized grammar must search a large set of supertags to choose the right ones to combine for the parse of the sentence. We present techniques for disambi...

We describe experiments into the use of distributional similarity for acquiring lexical information from clinical free text, in particular notes typed by primary care physicians (general practitioners). We also present a novel approach to lexical acquisition from ‘sensitive’ text, which does not require the text to be manually anonymised – a very expensive process – and therefore allows much la...

This paper presents a machine learning approach for paraphrase identification which uses lexical and semantic similarity information. In the experimental studies, we examine the limitations of the designed attributes and the behavior of three machine learning classifiers. With the objective to increase the final performance of the system, we scrutinize the influence of the combination of lexica...

In this work we test the use of word similarity lists for anaphora resolution in Portuguese corpora. We applied an automatic lexical acquisition technique over parsed texts to identify semantically similar words. After that, we made use of this lexical knowledge to resolve coreferent definite descriptions where the head-noun of the anaphor is different from the head-noun of its antecedent, whic...