Huge automatically extracted training sets for multilingual Word Sense Disambiguation

We release to the community six large-scale sense-annotated datasets in multiple language to pave the way for supervised multilingual Word Sense Disambiguation. Our datasets cover all the nouns in the English WordNet and their translations in other languages for a total of millions of sense-tagged sentences. Experiments prove that these corpora can be effectively used as training sets for supervised WSD systems, surpassing the state of the art for low- resourced languages and providing competitive results for English, where manually annotated training sets are accessible. The data is available at trainomatic. org

Two knowledge-based methods for High-Performance Sense Distribution Learning

Knowing the correct distribution of senses within a corpus can potentially boost the performance of Word Sense Disambiguation (WSD) systems by many points. We present two fully automatic and language-independent methods for computing the distribution of senses given a raw corpus of sentences. Intrinsic and extrinsic evaluations show that our methods outperform the current state of the art in sense distribution learning and the strongest baselines for the most frequent sense in multiple languages and on domain-specific test sets. Our sense distributions are available at http://trainomatic.org

Word sense disambiguation criteria: a systematic study

This article describes the results of a systematic in-depth study of the criteria used for word sense disambiguation. Our study is based on 60 target words: 20 nouns, 20 adjectives and 20 verbs. Our results are not always in line with some practices in the field. For example, we show that omitting non-content words decreases performance and that bigrams yield better results than unigrams

RDF/S)XML Linguistic Annotation of Semantic Web Pages

Although with the Semantic Web initiative much research on web pages semantic annotation has already done by AI researchers, linguistic text annotation, including the semantic one, was originally developed in Corpus Linguistics and its results have been somehow neglected by AI. ..

Combining Knowledge- and Corpus-based Word-Sense-Disambiguation Methods

In this paper we concentrate on the resolution of the lexical ambiguity that arises when a given word has several different meanings. This specific task is commonly referred to as word sense disambiguation (WSD). The task of WSD consists of assigning the correct sense to words using an electronic dictionary as the source of word definitions. We present two WSD methods based on two main methodological approaches in this research area: a knowledge-based method and a corpus-based method. Our hypothesis is that word-sense disambiguation requires several knowledge sources in order to solve the semantic ambiguity of the words. These sources can be of different kinds--- for example, syntagmatic, paradigmatic or statistical information. Our approach combines various sources of knowledge, through combinations of the two WSD methods mentioned above. Mainly, the paper concentrates on how to combine these methods and sources of information in order to achieve good results in the disambiguation. Finally, this paper presents a comprehensive study and experimental work on evaluation of the methods and their combinations

Evaluating large-scale knowledge resources across languages

This paper presents an empirical evaluation in a multilingual scenario of the semantic knowledge present on publicly available large-scale knowledge resources. The study covers a wide range of manually and automatically derived large-scale knowledge resources for English and Spanish. In order to establish a fair and neutral comparison, the knowledge resources are evaluated using the same method on two Word Sense Disambiguation tasks (Senseval-3 English and Spanish Lexical Sample Tasks). First, this study empirically demonstrates that the combination of the knowledge contained in these resources surpass the most frequent sense classi er for English. Second, we also show that this large-scale topical knowledge acquired from one language can be successfully ported to other languages.Peer ReviewedPostprint (author’s final draft

Multilingual evaluation of KnowNet

Este artículo presenta un nuevo método totalmente automático de construcción de bases de conocimiento muy densas y precisas a partir de recursos semánticos preexistentes. Básicamente, el método usa un algoritmo de Interpretación Semántica de las palabras preciso y de amplia cobertura para asignar el sentido mas apropiado a grandes conjuntos de palabras de un mismo tópico que han sido obtenidas de la web. KnowNet, la base de conocimiento resultante que conecta grandes conjuntos de conceptos semánticamente relacionados es un paso importante hacia la adquisición automática de conocimiento a partir de corpus. De hecho, KnowNet es varias veces mas grande que cualquier otro recurso de conocimiento disponible que codifique relaciones entre sentidos, y el conocimiento que KnowNet contiene supera cualquier otro recurso cuando es empíricamente evaluado en un marco multilingüe común. This paper presents a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method uses a wide-coverage and accurate knowledge-based Word Sense Disambiguation Algorithm to assign the most appropriate senses to large sets of topically related words acquired from the web. KnowNet, the resulting knowledge-base which connects large sets of semantically-related concepts is a major step towards the autonomous acquisition of knowledge from raw corpora. In fact, KnowNet is several times larger than any available knowledge resource encoding relations between synsets, and the knowledge KnowNet contains outperform any other resource when is empirically evaluated in a common multilingual framework.Peer ReviewedPostprint (published version