Grammatical feature engineering for fine-grained IR tasks
Danilo Croce, Roberto Basili · 2012
Abstract. Information Retrieval tasks include nowadays more and more complex information in order to face contemporary challenges such as Opinion Mining (OM) or Question Answering (QA). These are examples of tasks where complex linguistic information is required for reasonable performances on realistic data sets. As natural language learning is usually applied to these tasks, rich structures, such as parse trees, are critical as they require complex resources and accurate pre-processing. In this paper, we show how good quality language learning methods can be applied to the above tasks by using grammatical representations simpler than parse trees. These features are here shown to achieve the state-of-art accuracy in different IR tasks, such as OM and QA. 1 Syntactic modeling of linguistic features in Semantic Tasks Information Retrieval faces nowadays contemporary challenges such as Sentiment Analysis (SA) or Question Answering (QA), that are tight to complex and fine grained linguistic information. The traditional view in IR that represents the meaning of documents just according to the words that occur in them is not directly applicable. Statistical models,