tailieunhanh - Báo cáo khoa học: "Morphological Cues for Lexical Semantics"

Most natural language processing tasks require lexical semantic information. Automated acquisition of this information would thus increase the robustness and portability of NLP systems. This paper describes an acquisition method which makes use of fixed correspondences between derivational affixes and lexical semantic information. One advantage of this method, and of other methods that rely only on surface characteristics of language, is that the necessary input is currently available. | Morphological Cues for Lexical Semantics Marc Light Seminar fur Sprachwissenschaft Uni ver sit at Tubingen Wilhelmstr. 113 D-72074 Tubingen Germany Abstract Most natural language processing tasks require lexical semantic information. Automated acquisition of this information would thus increase the robustness and portability of NLP systems. This paper describes an acquisition method which makes use of fixed correspondences between derivational affixes and lexical semantic information. One advantage of this method and of other methods that rely only on surface characteristics of language is that the necessary input is currently available. 1 Introduction Some natural language processing NLP tasks can be performed with only coarse-grained semantic information about individual words. For example a system could utilize word frequency and a word cooccurrence matrix in order to perform information retrieval. However many NLP tasks require at least a partial understanding of every sentence or utterance in the input and thus have a much greater need for lexical semantics. Natural language generation providing a natural language front end to a database information extraction machine translation and task-oriented dialogue understanding all require lexical semantics. The lexical semantic information commonly utilized includes verbal argument structure and selectional restrictions corresponding nominal semantic class verbal aspectual class synonym and antonym relationships between words and various verbal semantic features such as causation and manner. Machine readable dictionaries do not include much of this information and it is difficult and time consuming to encode it by hand. As a consequence current NLP systems have only small lexicons and thus can only operate in restricted domains. Automated methods for acquiring lexical semantics could increase both the robustness and the portability of 25 such systems. In addition such methods might .