Finding support sentences for entities
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We study the problem of finding sentences that explain the relationship between a named entity and an ad-hoc query, which we refer to as entity support sentences. This is an important sub-problem of entity ranking which, to the best of our knowledge, has not been addressed before. In this paper we give the first formalization of the problem, how it can be evaluated, and present a full evaluation dataset. We propose several methods to rank these sentences, namely retrieval-based, entity-ranking based and position-based. We found that traditional bag-of-words models perform relatively well when there is a match between an entity and a query in a given sentence, but they fail to find a support sentence for a substantial portion of entities. This can be improved by incorporating small windows of context sentences and ranking them appropriately.
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Finding support sentences for entities
- 1. Finding Support Sentences for Entities Roi Blanco and Hugo Zaragoza Yahoo! Research Barcelona July, SIGIR 2010
- 2. Outline • Task definition • Features for ranking sentences • Context • Evaluation • SW1 dataset • TREC Novelty Track • Conclusions
- 4. www.evri.com
- 7. Entity Ranking • Given a topic, find relevant entities • Evaluated in TREC and INEX campaigns • Most well-known: people and expert search • Many other applications: dates, events, locations, companies, ...
- 8. Support Sentences for Entities • We introduce the task explaining the relationship between a query and an entity • Applications for entity retrieval, expert finding, object ranking, etc. • We don’t focus on entity ranking, just on the explanations • Support Sentences: Hqe(s) ~ p(R|q,e,s) • What makes a good sentence depends on how general entities and queries are and their relationship
- 9. PPPPiiciciccaaaassssssssoooo a aaannnndddd P PPPeeeeaaaacccceeee ss11 ((00..9900)) ss22 ((00..8866)) ss33 ((00..8833)) ........ ss11 ((00..9900)) ss22 ((00..8866)) ss33 ((00..8833)) ........ ss11:: PPiiccaassssoo ((00..8811)),, OOccttoobbeerr 11888811 ((00..5522)) ss22:: SSppaanniisshh CCiivviill WWaarr ((00..7733)),, GGuueerrnniiccaa ((00..6655)) ss11:: PPiiccaassssoo ((00..8811)),, OOccttoobbeerr 11888811 ((00..5522)) ss22:: SSppaanniisshh CCiivviill WWaarr ((00..7733)),, GGuueerrnniiccaa ((00..6655)) 11994444 PPiiccaassssoo SSppaanniisshh CCiivviill WWaarr 11994444 PPiiccaassssoo SSppaanniisshh CCiivviill WWaarr
- 10. “Flying Circus”
- 11. Examples • Query: Picasso and Peace • Entity: 1944 • “In 1944 Picasso joined the French Communist Party, attended an international peace conference in Poland, and in 1950 received the Stalin Peace Prize from the Soviet government.”
- 12. Examples • Query: Picasso and Peace • Entity: Northern Spain “Although it was not conceived by the author as a representation of the disasters of war, but the Nazi bombing of Guernica (a town in Northern Spain), it is now considered an iconic representation of the disasters of war.”
- 13. • Top-k sentenCceso ren-rtanekixngt (we don’t issue any subsequent queries) • Vocabulary mismatch problem (support sentences that do not contain any query term) • Entity supported must be in the sentence • Introduce small windows of context sentences
- 15. Features for Ranking • Top-k sentences • Augmented • Entity-candidate set • Using sentence scores: • Sentence score for the [query,sentence], BM25 • Sentence score for the [query, sentence + context], BM25F • Position:
- 16. • Aggregation of entity scores • sum, max, min, average, ... • Options for the entity ranker score E(q,e) • Frequency • Rarity • Combination • KLD
- 17. Evaluation Framework • Semantically Annotated Snapshot of the English Wikipedia (sentences + annotations) • 12 types from WSJ tag-set • Judges produce a set of queries and remove non-relevant entities • Evaluate a set of sentences using a 4-grade scale • 226 (entity,query) with 45 unique queries
- 18. Results
- 20. Novelty Track 2003 2004
- 21. Conclusions • We introduced the task of finding support sentences for entities (aka “entity snippets”) • We engineered several features based on scores of sentences and entities • We developed an evaluation dataset • http://barcelona.research.yahoo.net/dokuwiki • Evaluated the task and the role of context sentences