In the context of the development of a feature-based opinion mining system for English, we observed that there is some very interesting information provided by customers but not yet covered by "standard" opinion mining techniques: opinion mining aims at detecting whether comments are positive or negative, but it appears that customers are very often suggesting improvements about what they are reviewing, which is quite different from expressing an opinion. This papers proposes to apply Natural Language Processing techniques in order to address this rather new task of extracting automatically such kind of suggestions for improvement from user's comments.

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We describe an ongoing work in information extraction which is seen as a text normalization task. The normalized representation can be used to detect paraphrases in texts. Normalization and paraphrase detection tasks are built on top of a robust analyzer for English and are exclusively achieved using symbolic methods. Both grammar development rules and information extraction rules are expressed within the same formalism and are developed in an integrated way. The experiment we describe in the paper is evaluated and presents encouraging results.

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