Catálogo de publicaciones - libros

Thesaurus is a lexical resource which groups terms by semantic relatedness. It is shortcoming that the relations are ambiguous, in that it does not them; it only shows that there a relation between terms. Our work focuses on disambiguating hypernym relations within Thesaurus. Several techniques of identifying hypernym relations are compared and contrasted in this paper, and a total of over 50,000 hypernym relations have been disambiguated within . Human judges have evaluated the quality of our disambiguation techniques, and we have demonstrated on several applications the usefulness of the disambiguated relations.

In the paper, we present the results of an experiment with comparing the effectiveness of real text parsers of Czech language based on completely different approaches – stochastic parsers that provide dependency trees as their outputs and a meta-grammar parser that generates a resulting chart structure representing a packed forest of phrasal derivation trees.

We describe and formulate main questions and problems accompanying such experiment, try to offer answers to these questions and finally display also factual results of the tests measured on 10 thousand Czech sentences.

The paper deals with development and application of automatic word clustering (AWC) tool aimed at processing Russian texts of various types, which should satisfy the requirements of flexibility and compatibility with other linguistic resources. The construction of AWC tool requires computer implementation of latent semantic analysis (LSA) combined with clustering algorithms. To meet the need, Python-based software has been developed. Major procedures performed by AWC tool are segmentation of input texts and context analysis, co-occurrence matrix construction, agglomerative and -means clustering. Special attention is drawn to experimental results on clustering words in raw texts with changing parameters.

In this paper we present the results of our experiments with modifications of the feature set used in the Czech mutation of the Maximum Spanning Tree parser. First we show how new feature templates improve the parsing accuracy and second we decrease the dimensionality of the feature space to make the parsing process more effective without sacrificing accuracy.

We present experiments with a variety of corpus-based measures applied to the problem of constructing semantic similarity functions for Polish nouns. Rich inflection in Polish allows us to acquire useful syntactic features without parsing; morphosyntactic restrictions checked in a large enough window provide sufficiently useful data. A novel feature selection method gives the accuracy of 86% on the WordNet-based synonymy test, an improvement of 5% over the previous results.

This paper is focused on discovering bilingual news clusters in a comparable corpus. Particularly, we deal with the news representation and with the calculation of the similarity between documents. We use as representative features of the news the cognate named entities they contain. One of our main goals consists of proving whether the use of only named entities is a good source of knowledge for multilingual news clustering. In the vectorial news representation we take into account the category of the named entities. In order to determine the similarity between two documents, we propose a new approach based on a fuzzy system, with a knowledge base that tries to incorporate the human knowledge about the importance of the named entities category in the news. We have compared our approach with a traditional one obtaining better results in a comparable corpus with news in Spanish and English.

This paper presents the comparison between three methods for extractive summarization of Portuguese broadcast news: feature-based, Maximal Marginal Relevance, and Latent Semantic Analysis. The main goal is to understand the level of agreement among the automatic summaries and how they compare to summaries produced by non-professional human summarizers. Results were evaluated using the ROUGE-L metric. Maximal Marginal Relevance performed close to human summarizers. Both feature-based and Latent Semantic Analysis automatic summarizers performed close to each other and worse than Maximal Marginal Relevance, when compared to the summaries done by the human summarizers.

WordNet has become an important and useful resource for the natural language processing field. Recently, many countries have been developing their own WordNet. In this paper we show an evaluation of the Korean WordNet (U-WIN). The purpose of the work is to study how well the manually created lexical taxonomy U-WIN is built. Evaluation is done level by level, and the reason for selecting words for each level is that we want to compare each level and to find relations between them. As a result the words at a certain level (level 6) give the best score, for which we can make a conclusion that the words at this level are better organized than those at other levels. The score decreases as the level goes up or down from this particular level.

Our research is about an adaptive keyboard, which autonomously adjusts its predictive features and key displays to current user input. We used personalized word prediction to improve the performance of such a system. Prediction using common English dictionary (represented by the British National Corpus) is compared with prediction using personal data, such as personal documents, chat logs, and personal emails. A user study was also conducted to gather requirements for a new keyboard design. Based on these studies, we developed a personalized and adaptive on-screen keyboard for both single-handed and zero-handed users. It combines tapping-based and motion-based text input with language-based acceleration techniques, including personalized and adaptive task-based dictionary, frequent character prompting, word completion, and grammar checker with suffix completion.

An all-path parsing algorithm for a constraint-based dependency grammar of context-free power is presented. The grammar specifies possible dependencies between words together with a number of constraints. The algorithm builds a packed representation of ambiguous syntactic structure in the form of a dependency graph. For certain types of ambiguities the graph grows slower than the chart or parse forest.