Identification of the certainty of events is an important text mining problem. In particular, biomedical texts report medical conditions or findings that might be factual, hedged or negated. Identification of negation and its scope over a term of interest determines whether a finding is reported and is a challenging task. Not much work has been performed for Spanish in this domain. In this work we introduce different algorithms developed to determine if a term of interest is under the scope of negation in radiology reports written in Spanish. The methods include syntactic techniques based in rules derived from PoS tagging patterns, constituent tree patterns and dependency tree patterns, and an adaption of NegEx, a well known rule-based negation detection algorithm (Chapman et al., 2001a). All methods outperform a simple dictionary lookup algorithm developed as baseline. NegEx and the PoS tagging pattern method obtain the best results with 0.92 F1.

Term extraction may be defined as a text mining activity whose main purpose is to obtain all the terms included in a text of a given domain. Since the eighties, and mainly due to the rapid scientific advances as well as the evolution of the communication systems, there has been a growing interest in obtaining the terms found in written documents. A number of techniques and strategies have been proposed for satisfying this requirement. At present it seems that term extraction has reached a maturity stage. Nevertheless, many of the systems proposed fail to qualitatively present their results, almost every system evaluates its abilities in an ad hoc manner (if any, many times). Often, the authors do not explain their evaluation methodology; therefore comparisons between different implementations are difficult to draw. In this paper, we review the state-of-the-art of term extraction systems evaluation in the framework of natural language systems evaluation. The main approaches are presented, with a focus on their limitations. As an instantiation of some ideas for overcoming these limitations, the evaluation framework is applied to YATE, a hybrid term extractor.

A precise and commonly accepted definition of paraphrasing does not exist. This is one of the reasons that have prevented computational linguistics from a real success when dealing with this phenomenon in its systems and applications. With the aim of helping to overcome this difficulty, in this article, new insights on paraphrase characterization are provided. We first overview what has been said on paraphrasing from linguistics and the new lights shed on the phenomenon from computational linguistics. Under the light of the shortcomings observed, the paraphrase phenomenon is studied from two different perspectives. On the one hand, insights on paraphrase boundaries are set out analyzing paraphrase borderline cases and the interaction of paraphrasing with related linguistic phenomena. On the other hand, a new paraphrase typology is presented. It goes beyond a simple list of types and is embedded in a linguistically-based hierarchical structure. This typology has been empirically validated through corpus annotation and its application in the plagiarism-detection domain.

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from preexisting lexical resources. First, a set of automatic and complementary techniques for linking Spanish words collected from monolingual and bilingual MRDs to English WordNet synsets are described. Second, we show how resulting data provided by each method is then combined to produce a preliminary version of a Spanish WordNet with an accuracy over 85%. The application of these combinations results on an increment of the extracted connexions of a 40% without losing accuracy. Both coarsegrained (class level) and fine-grained (synset assignment level) confidence ratios are used and evaluated. Finally, the results for the whole process are presented. *

{ Imarquez, horacio}@ Is i. upc. esA b s t r a c t . We have appfied inductive learning of statistical decision trees to the Natural Language Processing (NLP) task of morphosyntactic disambiguation (Part Of Speech Tagging). Previous work showed that the acquired language models are independent enough to be easily incorporated, as a statistical core of rules, in any flexible tagger. They are also complete enough to be directly used as sets of POS disambiguation rules. We have implemented a quite simple and fast tagger that has been tested and evaluated on the Wall Street Journal (WSJ) corpus with a remarkable accuracy. In this paper we basically address the problem of tagging when only small training material is available, which is crucial in any process of constructing, from scratch, an annotated corpus. We show that quite high accuracy can be achieved with our system in this situation. In addition we also face the problem of dealing with unknown words under the same conditions of lacking training examples. In this case some comparative results and comments about close related work are reported. I n t r o d u c t i o n a n d S t a t e of t h e A r tPOS Tagging is a very well known NLP problem which consists of assigning to each word of a text the proper morphosyntactic tag in its context of appearance. Figure 1 shows the correct part of speech assignment to the words of a sentence, together with the list of valid labels for each word taken in isolation 1. The base of POS tagging is that being most words ambiguous regarding their POS, they can be almost completely disambiguated taking into account an adequate context.Starting with the pioneer tagger T A G G I T (Greene & Rubin 71), used for an initial tagging of the Brown Corpus (BC), a lot of efforts have been devoted to improve the quality of the tagging process in terms of accuracy and efficiency. Existing taggers can be classified into three main groups according to the kind of knowledge they use: linguistic, statistic and machine-learning family. Of course *