This paper presents the improvement process of a mention detector for Basque. The system is rule-based and takes into account the characteristics of mentions in Basque. A classification of error types is proposed based on the errors that occur during mention detection. A deep error analysis distinguishing error types and causes is presented and improvements are proposed. At the final stage, the system obtains an F-measure of 74.57% under the Exact Matching protocol and of 80.57% under Lenient Matching. We also show the performance of the mention detector with gold standard data as input, in order to omit errors caused by the previous stages of linguistic processing. In this scenario, we obtain an F-measure of 85.89% with Strict Matching and of 89.06% with Lenient Matching, i.e., a difference of 11.32 and 8.49 percentage points, respectively. Finally, how improvements in mention detection affect coreference resolution is analysed.

This paper presents a set of experiments for the detection and correction of syntactic errors, exploring two alternative approaches. The first one uses an error grammar which combines a robust morphosyntactic analyser and two groups of finite-state transducers (one for the description of syntactic error patterns and the other for the correction of the detected errors). We have also experimented an alternative approach using a positive date grammar where deviations are detected by applying edit-distance techniques. The system has been tested on a corpus of real texts which contained both correct and incorrect sentences. Although the experiment was limited to one language, the results show that attainable performance is not the only criterion for preferring one solution over another.

This paper focuses on the design methodology of the MultiLingual Dictionary-System (MLDS), which is a human-oriented tool for assisting in the task of translating lexical units, oriented to translators and conceived from studies carried out with translators. We describe the model adopted for the representation of multilingual dictionary-knowledge. Such a model allows an enriched exploitation of the lexical-semantic relations extracted from dictionaries. In addition, MLDS is supplied with knowledge about the use of the dictionaries in the process of lexical translation, which was elicitated by means of empirical methods and specified in a formal language. The dictionary-knowledge along with the task-oriented knowledge are used to offer the translator active, anticipative and intelligent assistance.

This paper discusses different issues in the construction and knowledge representation of an intelligent dictionary help system. The Intelligent Dictionary Help System (IDHS) is conceived as a monolingual (explanatory) dictionary system for human use (Artola and Evrard, 1992). The fact that it is intended for people instead of automatic processing distinguishes it from other systems dealing with the acquisition of semantic knowledge from conventional dictionaries. The system provides various access possibilities to the data, allowing to deduce implicit knowledge from the explicit dictionary information. IDHS deals with reasoning mechanisms analogous to those used by humans when they consult a dictionary. User level functionality of the system has been specified and a prototype has been implemented (Agirre et al., 1994a). A methodology for the extraction of semantic knowledge from a conventional dictionary is described. The method followed in the construction of the phrasal pattern hierarchies required by the parser (Alshawi, 1989) is based on an empirical study carried out on the structure of definition sentences. The results of its application to a real dictionary has shown that the parsing method is particularly suited to the analysis of short definition sentences, as it was the case of the source dictionary. As a result of this process, the characterization of the different lexical-semantic relations between senses is established by means of semantic rules (attached to the patterns); these rules are used for the initial construction of the Dictionary Knowledge Base (DKB). The representation schema proposed for the DKB (Agirre et al., 1994b) is basically a semantic network of frames representing word senses. After construction of the initial DKB, several enrichment processes are performed on the DKB to add new facts to it; these processes are based on the exploitation of the properties of lexical-semantic relations, and also on specially conceived deduction mechanisms. The result of the enrichment processes show the suitability of the representation schema chosen to deduce implicit knowledge. Erroneous deductions are mainly due to incorrect word sense disambiguation.