this document, text or image about?' Gradually moving from the left to the right in Figure 3.1, different understandings of this concept evolve

Abstract not found

this article. For the purpose of text classification, the answer keys serve only as a set of correct classifications for each text. If a text has instantiated key templates associated with it in the corpus, then it should be classified as a relevant text. If a text has no instantiated key templates associated with it (i.e., only a dummy template) then it should be classified as an irrelevant text. This is a binary classification problem: a text is either relevant to the terrorism domain or irrelevant. The texts were selected by keyword search from a database of newswire articles 2 because they contained words associated with terrorism. However, many of them did not mention any relevant terrorist incidents. Of the 1700 texts in the MUC4 corpus, only 53% described a relevant terrorist event. Because many of the texts in the corpus were irrelevant, the MUC-4 systems had to distinguish the relevant from the irrelevant texts. Although the MUC-4 task was information extraction, information detection 4 (i.e, text classification) was an implicit subtask. To be successful in MUC-4, the information extraction systems also had to be good at detection. Our MUC-4 system did not use a separate text classification module. Instead, we extracted information from every text and relied on a discourse analysis module to discard irrelevant templates. This strategy was very effective, 5 but it was expensive. A reliable text classification module could have filtered out irrele- 1MUC-3 was the Third Message Understanding ConferenCe held in 1991 [MUC-3 Proceedings 19911

m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but non-zero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of hand-constructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then hand-edited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on

Term clustering and syntactic phrase formation are methods for transforming natural language text. Both have had only mixed success as strategies for improving the quality of text representations for document retrieval. Since the strengths of these methods are complementary, we have explored combining them to produce superior representations. In this paper we discuss our implementation of a syntactic phrase generator, as well as our preliminary experiments with producing phrase clusters. These experiments show small improvements in retrieval effectiveness resulting from the use of phrase clusters, but it is clear that corpora much larger than standard information retrieval test collections will be required to thoroughly evaluate the use of this technique.

Most information retrieval systems use stopword lists and stemming algorithms. However, we have found that recognizing singular and plural nouns, verb forms, negation, and prepositions can produce dramatically different text classification results. We present results from text classification experiments that compare relevancy signatures, which use local linguistic context, with corresponding indexing terms that do not. In two different domains, relevancy signatures produced better results than the simple indexing terms. These experiments suggest that stopword lists and stemming algorithms may remove or conflate many words that could be used to create more effective indexing terms. Introduction Most information retrieval systems use a stopword list to prevent common words from being used as indexing terms. Highly frequent words, such as determiners and prepositions, are not considered to be content words because they appear in virtually every document. Stopword lists are almost univer...

. We present a natural language processing (NLP) approach to automatic indexing over controlled vocabulary which accounts for term variation. The approach combines a part of speech tagger, a generator of morphologically related forms, and a shallow transformational parser. The system is applied to the French language; it is trained on newspaper articles and tested on scientific literature. Precision rate of indexing on term and variants is 97.2%. It is only slightly lower than indexing without accounting for term variation (99.7%). Recall rate of indexing on term and variants (93.4%) is much higher than recall of indexing on term occurrences only (72.4%). Conflation of term variants increases indexing coverage up to 30%. The system is a convincing example of the potential synergy between full-fledged morphological analysis and local syntactic analysis. Many details are provided on the implementation of the system. Illustrative examples of syntactic transformations for the French language are given together with the theoretical and empirical methods for their formulation. 2 CHRISTIAN JACQUEMIN AND EVELYNE TZOUKERMANN 1.

This chapter presents a new domain of research and development in Natural Language Processing (NLP) that is concerned with the representation, acquisition, and recognition of terms. Terms are pervasive in scientific and technical documents; their identification is a crucial issue for any application dealing with the analysis, understanding, generation, or translation of such documents. In particular, the ever-growing mass of specialized documentation available on-line, in industrial and governmental archives or in digital libraries, calls for advances in terminology processing for such purposes as information retrieval, cross-language querying, indexing of multimedia documents, translation aids, document routing and summarization, etc. This chapter introduces the basic linguistic characteristics of terms. It presents the main methods in NLP for recognizing or discovering terms and their interrelationships in large corpora. It is divided into three sections: an introduction to the bas...

This paper discusses research on distinguishing word meanings in the context of information retrieval systems. We conducted experiments with three sources of evidence for making these distinctions: morphology, part-of-speech, and phrases. We have focused on the distinction between homonymy and polysemy (unrelated vs. related meanings). Our results support the need to distinguish homonymy and polysemy. We found: 1) grouping morphological variants makes a significant improvement in retrieval performance, 2) that more than half of all words in a dictionary that differ in part-of-speech are related in meaning, and 3) that it is crucial to assign credit to the component words of a phrase. These experiments provide a better understanding of word-based methods, and suggest where natural language processing can provide further improvements in retrieval performance. 1

This paper describes ROSA, a software reuse system based on the processing of the natural language descriptions of software artifacts. Lexical, syntactic and semantic analysis of software descriptions is performed to automatically extract both verbal and nominal phrases from descriptions and use this information to create frame-based indexing units for software components. Retrieval similarity measures provide good retrieval effectiveness by supporting semantic matching and processing of lexical relationships between terms. Some results from an experiment evaluating retrieval effectiveness are discussed. 1 Introduction This paper describes ROSA (Reuse Of Software Artifacts) a software reuse system based on the processing of the natural language descriptions of software artifacts [9][10][11][12]. The system aims at being cost-effective, domain independent and providing good retrieval effectiveness. Automatic indexing is required to turn software retrieval systems cost-effective. Reuse ...