This article presents the GETALP system for the participation to SemEval-2013 Task 12, based on an adaptation of the Lesk measure propagated through an Ant Colony Algorithm, that yielded good results on the corpus of Semeval 2007 Task 7 (WordNet 2.1) as well as the trial data for Task 12 SemEval 2013 (BabelNet 1.0). We approach the parameter estimation to our algorithm from two perspectives: ed...

synsets appear in the top of the hierarchy, while more specific ones are placed at the bot tom. Thus, Wu and Palmer [26] propose a similarity measure which includes the depth of the synsets and of the least common subsumer (see equation 1). The least common subsumer les is the deepest hypernym tha t subsumes both synsets, and depth is the length of the pa th from the root to the synset. This si...

Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the target word as the context for disambiguation because their computational co...

Although WordNets have been developed for a number of languages, no attempts to construct a Japanese WordNet have been known to exist. Taking this into account, we launched a project to automatically translate the Princeton WordNet into Japanese by a method of unsupervised word-sense disambiguation using bilingual comparable corpora. The method we propose aligns English word associations with t...

This paper presents the language resource management system for the development and dissemination of Asian WordNet (AWN) and its web service application. We develop the platform to establish a network for the cross language WordNet development. Each node of the network is designed for maintaining the WordNet for a language. Via the table that maps between each language WordNet and the Princeton...

Matching domain and top-level ontologies is an important task but still an open problem in the ontology matching field. The main difficulties are particularly due to their different levels of abstraction. In this paper, we propose an approach that exploits existing alignments between WordNet and top-level ontologies, as an intermediate layer, and that relies on the notion of context of concepts...

Automatic approaches to creating and extending wordnets, which have become very popular in the past decade, inadvertently result in noisy synsets. This is why we propose an approach to detect synset outliers in order to eliminate the noise and improve accuracy of the developed wordnets, so that they become more useful lexico-semantic resources for natural language applications. The approach com...

One of the objectives of sentiment analysis is to classify the polarity of conveyed opinions from the perspective of textual evidence. Most of the work in the field has been intensively applied to the English language and only few experiments have explored other languages. In this paper, we present a supervised classification of posts in French online forums where sentiment analysis is based on...

In this paper, we put forward a strategy that supplements Hindi WordNet entries with information on the temporality of its word senses. Each synset of Hindi WordNet is automatically annotated to one of the five dimensions: past, present, future, neutral and atemporal. We use semi-supervised learning strategy to build temporal classifiers over the glosses of manually selected initial seed synset...

We present our supervised sentiment classification system which competed in SemEval2015 Task 10B: Sentiment Classification in Twitter— Message Polarity Classification. Our system employs a Support Vector Machine classifier trained using a number of features including n-grams, dependency parses, synset expansions, word prior polarities, and embedding clusters. Using weighted Support Vector Machi...