In this paper, we propose a new methodology based on directed graphs and the TextRank algorithm to automatically induce general-specific noun relations from web corpora frequency counts. Different asymmetric association measures are implemented to build the graphs upon which the TextRank algorithm is applied and produces an ordered list of nouns from the most general to the most specific. Exper...

We propose a novel word embedding-based hypernym generation model that jointly learns clusters of hyponym-hypernym relations, i.e., hypernymy, and projections from hyponym to hypernym embeddings. Most of the recent hypernym detection models focus on a hypernymy classification problem that determines whether a pair of words is in hypernymy or not. These models do not directly deal with a hyperny...

We propose an intention analysis system for instant messaging applications. The system adopts Yahoo! directory as category trees, and classifies each dialogue into one of the categories of the directory. Two weighting schemes in information retrieval, i.e., tf and tf-idf, are considered in our experiments. In addition, we also expand Yahoo! directory with the accompanying HTML files and explore...

In this paper we describe the participation of the Joint Research Centre, EC, in task 14 Semantic Taxonomy Enrichment at SemEval 2016. The algorithm which we propose transforms each candidate definition into a term vector, where each dimension represents a term and its value is calculated by TF.IDF. We attach the candidate term as a hyponym to the WordNet synset with the most similar definition...

This paper reports on a study to explore how semantic relations can be used to expand a query for objects in an image. The study is part of a project ith the overall objective to provide semantic annotation and search facilities for a virtual collection of art resources. In this study we used semantic elations from WordNet for 15 image content queries. The results show that, next to the hyponym...

This paper describes our submission to the SemEval-2016 Taxonomy Extraction Evaluation (TExEval-2) Task. We examine the endocentric nature of hyponyms and propose a simple rule-based method to identify hypernyms at high precision. For the food domain, we extract lists of terms from the Wikipedia lists of lists by using the name of each list as the endocentric head and treating all terms in the ...

Information content plays an important role in measuring semantic similarity of concepts. The conventional way of IC obtained is through statistical analysis of corpora. Recently corpora–independent model has attracted great concern in this area. This paper analyzes the state-of-art IC models, highlights important related issues, and presents a novel IC model based on concepts’ topology in Word...

Some words are more contentful than others: for instance, make is intuitively more general than produce and fifteen is more ‘precise’ than a group. In this paper, we propose to measure the ‘semantic content’ of lexical items, as modelled by distributional representations. We investigate the hypothesis that semantic content can be computed using the KullbackLeibler (KL) divergence, an informatio...

Hypernym-hyponym (“is-a”) relations are key components in taxonomies, object hierarchies and knowledge graphs. While there is abundant research on is-a relation extraction in English, it still remains a challenge to identify such relations from Chinese knowledge sources accurately due to the flexibility of language expression. In this paper, we introduce a weakly supervised framework to extract...