Learning joint query interpretation and response ranking

By Uma Sawant, Soumen Chakrabarti. 2012

51 citations recorded by the source corpus.

Thanks to information extraction and semantic Web efforts, search on unstructured text is increasingly refined using semantic annotations and structured knowledge bases. However, most users cannot become familiar with the schema of knowledge bases and ask structured queries. Interpreting free-format queries into a more structured representation is of much current interest. The dominant paradigm is to segment or partition query tokens by purpose (references to types, entities, attribute names, attribute values, relations) and then launch the interpreted query on structured knowledge bases. Given that structured knowledge extraction is never complete, here we choose a less trodden path: a data representation that retains the unstructured text corpus, along with structured annotations (mentions of entities and relationships) on it. We propose two new, natural formulations for joint query interpretation and response ranking that exploit bidirectional flow of information between the knowledge base and the corpus. One, inspired by probabilistic language models, computes expected response scores over the uncertainties of query interpretation. The other is based on max-margin discriminative learning, with latent variables representing those uncertainties. In the context of typed entity search, both formulations bridge a considerable part of the accuracy gap between a generic query that does not constrain the type at all, and the upper bound where the "perfect" target entity type of each query is provided by humans. Our formulations are also superior to a two-stage approach of first choosing a target type using recent query type prediction techniques, and then launching a type-restricted entity search query.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension (2017, background): “, 2014), HTML tables (Pasupat and Liang, 2015), and large (and noisy) corpora (Sawant and Chakrabarti, 2013; Joshi et al., 2014; Xu et al., 2015).”
ReNoun: Fact Extraction for Nominal Attributes (2014, background): “…to queries about entities that the search engine knows about (e.g., famous individuals, organizations or locations) complement the links that the search en-∗ Work done during an internship at Google Research. gine typically returns (Sawant and Chakrabati, 2013; Singhal, 2012; Yahya et al., 2012).”
Knowledge Graph and Corpus Driven Segmentation and Answer Inference for Telegraphic Entity-seeking Queries (2014, methodology): “The second data set, TREC-INEX, from (Sawant and Chakrabarti, 2013) has about 700 queries sampled from TREC and INEX, available at http: //bit.ly/WSpxvr .”