Semantic Matching in Search

Li, Hang; Xu, Jun

doi:10.1561/1500000035

Cited by 156 publications

(90 citation statements)

References 20 publications

(24 reference statements)

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“…We show that the proposed approach improves retrieval performance compared to generative language models mainly due to its ability to perform semantic matching [7]. The proposed method does not require any annotations or supervised relevance judgments and is able to learn from raw textual evidence and document-candidate associations alone.…”

Section: Introductionmentioning

confidence: 93%

“…In the talk, which will be given by the second author, we will point out that existing methods to entity or expert retrieval fail to address key challenges: (1) Queries and expert documents use different representations to describe the same concepts [6,7]. Term mismatches between entities and experts [7] occur due to the inability of widely used maximum-likelihood language models to make use of semantic similarities between words [9].…”

Section: Introductionmentioning

confidence: 99%

“…Term mismatches between entities and experts [7] occur due to the inability of widely used maximum-likelihood language models to make use of semantic similarities between words [9]. (2) As the amount of available data increases, the need for more powerful approaches with greater learning capabilities than smoothed maximum-likelihood language models is obvious [13].…”

Section: Introductionmentioning

confidence: 99%

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Semantic Entities

Gysel

Rijke

Worring

2015

Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic Entities

Gysel

Rijke

Worring

2015

Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval

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“…For the majority of cases in which search engine users complain that they cannot find information, while the information does exist in the system, the reasons are due to a mismatch between terms in queries and documents [24]. Term mismatch happens because searchers and content creators often use different vocabularies and language styles to refer to the same concepts [14].…”

Section: Introductionmentioning

confidence: 99%

“…Term mismatch happens because searchers and content creators often use different vocabularies and language styles to refer to the same concepts [14]. To bridge this lexical gap between queries and documents, latent semantic models have been proposed [24].…”

Section: Introductionmentioning

confidence: 99%

Using Metafeatures to Increase the Effectiveness of Latent Semantic Models in Web Search

Borisov

Serdyukov

Rijke

2016

Proceedings of the 25th International Conference on World Wide Web

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Using metafeatures to increase the effectiveness of latent semantic models in web search Borisov, A.; Serdyukov, P.; de Rijke, M. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. ABSTRACTIn web search, latent semantic models have been proposed to bridge the lexical gap between queries and documents that is due to the fact that searchers and content creators often use different vocabularies and language styles to express the same concept. Modern search engines simply use the outputs of latent semantic models as features for a so-called global ranker. We argue that this is not optimal, because a single value output by a latent semantic model may be insufficient to describe all aspects of the model's prediction, and thus some information captured by the model is not used effectively by the search engine.To increase the effectiveness of latent semantic models in web search, we propose to create metafeatures-feature vectors that describe the structure of the model's prediction for a given querydocument pair-and pass them to the global ranker along with the models' scores. We provide simple guidelines to represent the latent semantic model's prediction with more than a single number, and illustrate these guidelines using several latent semantic models.We test the impact of the proposed metafeatures on a web document ranking task using four latent semantic models. Our experiments show that (1) through the use of metafeatures, the performance of each individual latent semantic model can be improved by 10.2% and 4.2% in NDCG scores at truncation levels 1 and 10; and (2) through the use of metafeatures, the performance of a combination of latent semantic models can be improved by 7.6% and 3.8% in NDCG scores at truncation levels 1 and 10, respectively.

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Enhancing information retrieval through concept‐based language modeling and semantic smoothing

L'Hadj

Amrouche

2015

Asso for Info Science & Tech

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Traditionally, many information retrieval models assume that terms occur in documents independently. Although these models have already shown good performance, the word independency assumption seems to be unrealistic from a natural language point of view, which considers that terms are related to each other. Therefore, such an assumption leads to two well‐known problems in information retrieval (IR), namely, polysemy, or term mismatch, and synonymy. In language models, these issues have been addressed by considering dependencies such as bigrams, phrasal‐concepts, or word relationships, but such models are estimated using simple n‐grams or concept counting. In this paper, we address polysemy and synonymy mismatch with a concept‐based language modeling approach that combines ontological concepts from external resources with frequently found collocations from the document collection. In addition, the concept‐based model is enriched with subconcepts and semantic relationships through a semantic smoothing technique so as to perform semantic matching. Experiments carried out on TREC collections show that our model achieves significant improvements over a single word‐based model and the Markov Random Field model (using a Markov classifier).

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Semantic Matching in Search

Cited by 156 publications

References 20 publications

Semantic Entities

Semantic Entities

Using Metafeatures to Increase the Effectiveness of Latent Semantic Models in Web Search

Enhancing information retrieval through concept‐based language modeling and semantic smoothing

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