A coupled linguistics/statistical technique for query structure classification and its application to query expansion

Bhawani Selvaretnam, Mohammed Belkhatir, Christopher Messom

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


The retrieval effectiveness of Query Expansion (QE) is very much dependent on the ability to accurately identify and expand core concepts which are truly representative of the intended search goal. Two characteristics of natural language queries which hinder the performance of query expansion for information retrieval are query length and structure. The varying lengths of a query translate to the number of core concepts that may exist and the possibility of there being multiple query intents embedded within a single query. On the other hand, the structure of queries reveals the linguistic properties which allows for the determination of whether they take the form of well-formed sentences or are simply bags-of-words which in the strictest sense are a series of words with no obvious relations amongst them. Whilst query lengths are easily assessed, we propose a two-level automated classification technique consisting of linguistics based and statistical processing for query structure classification. The proposed method has revealed high levels of classification accuracy on TREC ad hoc test queries.

Original languageEnglish
Title of host publicationProceedings of the 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2013
EditorsJianhua Chen, Xingwei Wang, Lipo Wang, Jinguang Sun, Xiangfu Meng
Place of PublicationPiscataway NJ
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Print)9781467352536
Publication statusPublished - 2013
EventInternational Conference on Fuzzy Systems and Knowledge Discovery 2013 - Shenyang, China
Duration: 23 Jul 201325 Jul 2013
Conference number: 10th


ConferenceInternational Conference on Fuzzy Systems and Knowledge Discovery 2013
Abbreviated titleFSKD 2013


  • Query structure classification
  • Natural language processing
  • Query expansion
  • Information retrieval

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