Several approaches have been described for the automatic unsupervised acquisition of patterns for information extraction.

Abstract not found

In this paper, we discuss the performance of crosslingual information extraction systems employing an automatic pattern acquisition module. This module, which creates extraction patterns starting from a user's narrative task description, allows rapid customization to new extraction tasks. We compare two approaches: (1) acquiring patterns in the source language, performing source language extraction, and then translating the resulting templates to the target language, and (2) translating the texts and performing pattern discovery and extraction in the target language. We demonstrate an average of 8-10% more recall using the first approach. We discuss some of the problems with machine translation and their effect on pattern discovery which lead to this difference in performance.

We report evaluation results for our summarization system and analyze the resulting summarization data for three different types of corpora. To develop a robust summarization system, we have created a system based on sentence extraction and applied it to summarize Japanese and English newspaper articles, obtained some of the top results at two evaluation workshops.

We report evaluation results for our summarization system and analyze the resulting summarization data for three different types of corpora. To develop a robust summarization system, we have created a system based on sentence extraction and applied it to summarize Japanese and English newspaper articles, obtained some of the top results at two evaluation workshops. We have also created sentence extraction data from Japanese lectures and evaluated our system with these data. In addition to the evaluation results, we analyze the relationships between key sentences and the features used in sentence extraction. We find that discrete combinations of features match distributions of key sentences better than sequential combinations. 1