Formal language models for finding groups of experts

Liang, Shangsong; Rijke, Maarten de

doi:10.1016/j.ipm.2015.11.005

Cited by 35 publications

(14 citation statements)

References 39 publications

(69 reference statements)

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“…Product search is a particular example of the more general entity finding task that is increasingly being studied. Other entity finding tasks considered recently include searching for people [6], books [23] and groups [35]. Products are retrievable entities where every product is associated with a description and one or more user reviews.…”

Section: Introductionmentioning

confidence: 99%

Learning Latent Vector Spaces for Product Search

Gysel

Rijke

Kanoulas

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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126

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We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular word. We compare our method to existing latent vector space models (LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank setting. Our latent vector space model achieves its enhanced performance as it learns better product representations. Furthermore, the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation. We provide an in-depth analysis of the performance of our model and analyze the structure of the learned representations.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 201

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Section: Introductionmentioning

confidence: 99%

Learning Latent Vector Spaces for Product Search

Gysel

Rijke

Kanoulas

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Self Cite

126

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“…In comparison, we implement two other solutions of entity type ranking. According to the results, the perforamnce of the DGQ model in [7] (Base1) is far from the best performance achieved by our type ranking model because DGQ ig- nores the hypernym-hyponym relationship between the type and the query headwords. According to the method [10], we detect the headword from a query as the query entity type and annotate it as Base2.…”

Section: Experimental Evaluationmentioning

confidence: 89%

ESearch

Chen

et al. 2017

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

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The paper introduces an open domain entity search system called ESearch, which aims at finding a list of relevant entities to an open domain entity search query (a natural language question). The system is built on top of a Wikipedia text corpus, as well as the structured DBPedia knowledge base. Entities are initially ranked by a model which effectively associates context matching (based on the contexts of entities in the unstructured text corpus) and category matching (based on the types of entities in the structured knowledge base). They are ranked further by a re-ranking component supported by blind feedback or user feedback on entities. We show that category matching is critical for the search performance and the re-ranking component can boost the performance largely. Category matching therefore needs some query entity types (especially specific entity types) as input. However, it is often hard for systems to detect specific entity types because users may not be familiar with how the types of desired entities are defined in the structured knowledge base. In ESearch, we design an effective ranking model of entity types to facilitate blind feedback and user feedback on desired entity types for category matching, so that users can effectively perform entity search without the need of explicitly providing any query entity types as inputs.

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“…Also, reference [109] used generative probabilistic and similarity models to propose Beneficial Collaborator Recommendation (BCR). Also, reference [77] used generative language models for group finding task from heterogeneous document repository.…”

Section: Generative Probabilistic Modelsmentioning

confidence: 99%

“…Two studies [80,81] used medical documents. Moreover, another two studies [15,77] used employee documents. Also, a bug repository was used as expertise evidence by the authors in reference [36].…”

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confidence: 99%

Expert Finding Systems: A Systematic Review

et al. 2019

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The data overload problem and the specific nature of the experts’ knowledge can hinder many users from finding experts with the expertise they required. There are several expert finding systems, which aim to solve the data overload problem and often recommend experts who can fulfil the users’ information needs. This study conducted a Systematic Literature Review on the state-of-the-art expert finding systems and expertise seeking studies published between 2010 and 2019. We used a systematic process to select ninety-six articles, consisting of 57 journals, 34 conference proceedings, three book chapters, and one thesis. This study analyses the domains of expert finding systems, expertise sources, methods, and datasets. It also discusses the differences between expertise retrieval and seeking. Moreover, it identifies the contextual factors that have been combined into expert finding systems. Finally, it identifies five gaps in expert finding systems for future research. This review indicated that ≈65% of expert finding systems are used in the academic domain. This review forms a basis for future expert finding systems research.

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Formal language models for finding groups of experts

Cited by 35 publications

References 39 publications

Learning Latent Vector Spaces for Product Search

Learning Latent Vector Spaces for Product Search

ESearch

Expert Finding Systems: A Systematic Review

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