We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associated with pattern-based extractions: popularity and productivity. Intuitively, a candidate is popular if it was discovered many times by other instances in the hyponym pattern. A candidate is productive ...

This study investigates the depth of lexical knowledge in first language (L1) speakers and second language (L2) learners in reference to hierarchical word knowledge. Eighty-eight participants took part in a lexical decision task that assessed their speed and accuracy in recognizing words and nonwords. Prime and target pairs in the lexical decision task were related words (hyponynm to hypernym a...

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 meas...

We explore how lexical and ontological relations can be acquired automatically from natural language questions. The focus in this paper is on identifying hyponym and meronym relations by using simple pattern matching. It is shown that natural language questions can provide a significant source for ontological information.

Many lexical semantic relations, such as the hyponymy relation, can be extracted from text as they occur in detectable syntactic constructions. This paper shows how a hypernym-hyponym based lexicon for Swedish can be created directly from a news paper corpus. An algorithm is presented for building partial hierarchical structures from non domain-specific texts.

This paper presents a method for word sense disambiguation and coherence understanding of prepositional relations. The method relies on information provided by WordNet 1.5. We rst classify preposi-tional attachments according to semantic equivalence of phrase heads and then apply inferential heuristics for understanding the validity of prepositional structures. 1 Problem description In this pap...

The paper describes a rule-based approach for hypernym and hyponym extraction from Russian texts. For this task we employ finite state transducers (FSTs). We developed 6 finite state transducers that encode 6 lexicosyntactic patterns, which show a good precision on Russian DBpedia: 79.5% of the matched contexts are correct.