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Information Processing and Management, 36(2):207-227. Real life, real users, and real needs: a study and analysis of user queries on the web. Jansen, Amanda Spink, and Tefko Saracevic. The effect of part-of-speech tagging on ir performance for turkish. Bekir Taner Dincer and Bahar Karaoglan.In Annual ACM Conference on Research and Development in Information Retrieval Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 145-152, Berkeley, California. Phrase recognition and expansion for short, precision-biased queries based on a query log. In Proceedings of the Sixth Text Retrieval Conference (TREC-6), pages 667-686. Short queries, natural language and spoken document retrieval: Experiments at glasgow university. Fabio Crestani, Mark Sanderson, and Mounia Lalmas.Improving information retrieval systems using part of speech tagging.
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Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. In Proceedings of the 9th ACM Conference on Electronic Commerce, pages 1-9, Chicago, Illinois. Natural language generation in sponsored-search advertisements. Kevin Bartz, Cory Barr, and Adil Aijaz.Using part-of-speech patterns to reduce query ambiguity. We conclude that training a part-of-speech tagger on labeled corpora of queries significantly outperforms taggers based on traditional corpora, and leveraging the unique linguistic structure of web-search queries can improve search experience. These experiments also include the potential use of the tagger in selecting words for omission or substitution in query reformulation, actions which can improve recall. In particular, we show that part-of-speech information can be a significant feature in machine-learned search-result relevance. We also conduct preliminary investigative experiments into the practical applicability of leveraging query-trained part-of-speech taggers for information-retrieval tasks. In addition, we investigate classification of search queries into grammatical classes based on the syntax of part-of-speech tag sequences. We then use a set of queries manually labeled with these tags to train a Brill tagger and evaluate its performance. We also show that the majority of queries are noun-phrases, not unstructured collections of terms. We find that proper-nouns constitute 40% of query terms, and proper nouns and nouns together constitute over 70% of query terms. We begin by identifying a set of part-of-speech tags suitable for search queries and quantifying their occurrence. In this paper we investigate the applicability of part-of-speech tagging to typical English-language web search-engine queries and the potential value of these tags for improving search results. Web-search queries are known to be short, but little else is known about their structure.