Abstractsmall Previous works on question classification are based on complex natural language processing techniques: named entity extractors, parsers, chunkers, etc. While these approaches have proven to be effective they have the disadvantage of being targeted to a particular language. We present here a simple approach that exploits lexical features and Internet to train a classifier, in particular a Support Vector Machine. The main feature of this method is that it can be applied to different languages without requiring major adaptation changes. Experimental results of this method on English, Italian and Spanish show that this approach can be a practical tool for question answering systems reaching classification accuracy as high as 88.92%.

Abstract. The language model is an important component of any speech recognition system. In this paper, we present a lexical enrichment methodology of corpora focused o n the construction of statistical language models. This methodology co nsiders, on one hand, the identification of the set of poor represented words of a given training corpus, and on the other hand, the enrichment of the given co rpus by the repetitive inclusion of selected text fragments containing these words. The first part of the paper describes the formal details about this methodology; the second part presents some experiments and results that validate our method.

Abstract. The problem of acquiring valuable information from the large amounts available today in electronic media requires automated mechanisms more natural and efficient than those already existing. The trend in the evolution of information retrieval systems goes toward systems capable of answering specific questions formulated by the user in her/his language. The expected answers from such systems are short and accurate sentences, instead of large document lists. On the other hand, the state of the art of these systems is focused -mainly-in the resolution of factual questions, whose answers are named entities (dates, quantities, proper nouns, etc). This paper proposes a model to represent source documents that are then used by question answering systems. The model is based on a representation of a document as a set of named entities (NEs) and their local lexical context. These NEs are extracted and classified automatically by an off-line process. The entities are then taken as instance concepts in an upper ontology and stored as a set of DAML+OIL resources which could be used later by question answering engines. The paper presents a case of study with a news collection in Spanish and some preliminary results.

Abstract. This paper describes the prototype developed in the Language Technologies Laboratory at INAOE for the Spanish monolingual QA evaluation task at CLEF 2005. The proposed approach copes with the QA task according to the type of question to solve (factoid or definition). In order to identify possible answers to factoid questions, the system applies a methodology centered in the use of lexical features. On the other hand, the system is supported by a pattern recognition method in order to identify answers to definition questions. The paper shows the methods applied at different stages of the system, with special emphasis on those used for answering factoid questions. Then the results achieved with this approach are discussed.

Abstract. We present in this work a method for question classification in Spanish and Portuguese. The method relies on lexical features and attributes extracted from the Web. A machine learning algorithm, namely Support Vector Machines is successfully trained on these features. Our experimental results show that this method performs consistently well over two different languages.