Dynamic Collective Entity Representations for Entity Ranking

Graus, David; Tsagkias, Manos; Weerkamp, Wouter; Meij, Edgar; Rijke, Maarten de

doi:10.1145/2835776.2835819

Cited by 20 publications

(13 citation statements)

References 40 publications

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“…It does so in an unsupervised and automatic manner such that words that are strongly evidential for particular products are projected nearby those products. While engineering of representations is important in information retrieval [2,10,12,16,25,61], unsupervised joint representation learning of words and entities has not received much attention. We fill this gap.…”

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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“…Ritchie et al [166] expanded this line of work for research literature search by using the text associated with citations as a form of anchor text to expand document representations. Currently, anchor text is considered to be a gold standard for the social expansion of document representations, and it is typically used as a baseline against which to evaluate alternative social expansion approaches [14,151,123,87].…”

Section: Link Anchorsmentioning

confidence: 99%

“…The idea of using failed queries for document expansion was originally suggested by Furnas [80] and was based on explicit user feedback. The use of automatic query-based document expansion based on "smart" session mining was suggested by Amitay et al [14] and independently explored in a few other projects [61,87]. Currently, "smart" mining of query sessions serves as one of the main sources for both social indexing and document ranking.…”

Section: Query Logsmentioning

confidence: 99%

Social Search

Brusilovsky

Smyth

Shapira

2018

Social Information Access

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Today, most people find what they are looking for online by using search engines such as Google, Bing, or Baidu. Modern web search engines have evolved from their roots in information retrieval to developing new ways to cope with the unique nature of web search. In this chapter, we review recent research that aims to make search a more social activity by combining readily available social signals with various strategies for using these signals to influence or adapt more conventional search results. The chapter begins by framing the social search landscape in terms of the sources of data available and the ways in which this can be leveraged before, during, and after search. This includes a number of detailed case studies that serve to mark important milestones in the evolution of social search research and practice.

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“…It is also possible to construct hybrid representations. For example, Graus et al [34] combine various entity description sources, including a knowledge base, web anchors, social media, and search queries. Ranking without direct entity representations is also feasible, as we have discussed in Sect.…”

Section: Further Readingmentioning

confidence: 99%

Term-Based Models for Entity Ranking

Balog

2018

Entity-Oriented Search

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We have established in Chap. 1 that entities are natural and meaningful units of retrieval. To recap, according to our working definition, an entity is a uniquely identifiable "thing," a typed object, with name(s), attributes, and relationships to other entities. Examples of some of the most frequent types of entities include people, locations, organizations, products, and events. Returning specific entities, instead of a mere list of documents, can provide better answers to a broad range of information needs. The way these information needs are expressed may vary from short keyword queries to full-fledged natural language questions (cf. Sect. 1.3.1.1). In this chapter, we adhere to the "single search box" paradigm, which accepts "free text" search queries, and simply treat queries as sequences of words, referred to hereinafter as terms. The task we are going to focus on is ad hoc entity retrieval: answering a one-off free text query, representing the user's underlying information need, with a ranked list of entities. The fundamental question we are concerned with then is: How do we perform relevance matching of queries against entities?In Chap. 2, we have introduced large-scale knowledge repositories, like Wikipedia and DBpedia, that are devoted to organizing information around entities in a (semi-)structured format. Having information accumulated about entities in a knowledge repository is indeed helpful, yet it is not a prerequisite. As we shall see later in this chapter, it is possible to rank entities without (pre-existing) direct representations, as long as they can be recognized and identified uniquely in documents. The main idea of this chapter can be summarized as follows: If textual representations can be constructed for entities, then the ranking of these representations ("entity descriptions") becomes straightforward by building on traditional document retrieval techniques (such as language models or BM25). Accordingly, the bulk of our efforts in this chapter, described in Sect. 3.2, will revolve around assembling term-based entity representations from various sources, ranging from unstructured to structured. In the simplest case, these entity representations are based on the bag-of-words model, which considers the frequency of words but disregards their order. We will also introduce an extension to multiple document fields, as a mechanism for preserving some of the structure associated with entities.

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Dynamic Collective Entity Representations for Entity Ranking

Cited by 20 publications

References 40 publications

Learning Latent Vector Spaces for Product Search

Learning Latent Vector Spaces for Product Search

Social Search

Term-Based Models for Entity Ranking

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