Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

Xiong, Chenyan; Liu, Zhengzhong; Callan, Jamie; Liu, Tie-Yan

doi:10.1145/3209978.3209982

Cited by 34 publications

(22 citation statements)

References 28 publications

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“…Knowledge-base Expansion Models Recent previous work demonstrates that query expansion using external knowledge sources and entity annotations can lead to significant improvements to a variety of retrieval tasks [4], including entity linking of queries [8], and using entity-derived language models for document representation [18]. There is also recent work on determining the salience of entities in documents [24] for ranking. Beyond salience, research focused on identifying latent entities [10], [22] and connecting the query-document vocabularies in a latent space.…”

Section: Background and Related Workmentioning

confidence: 99%

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Local and Global Query Expansion for Hierarchical Complex Topics

Dalton

Naseri

Dietz

et al. 2019

Lecture Notes in Computer Science

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In this work we study local and global methods for query expansion for multifaceted complex topics. We study word-based and entity-based expansion methods and extend these approaches to complex topics using fine-grained expansion on different elements of the hierarchical query structure. For a source of hierarchical complex topics we use the TREC Complex Answer Retrieval (CAR) benchmark data collection. We find that leveraging the hierarchical topic structure is needed for both local and global expansion methods to be effective. Further, the results show that entity-based expansion methods show significant gains over word-based models alone, with local feedback providing the largest improvement. The results on the CAR paragraph retrieval task demonstrate that expansion models that incorporate both the hierarchical query structure and entity-based expansion result in a greater than 20% improvement over word-based expansion approaches.

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Section: Background and Related Workmentioning

confidence: 99%

“…Further, recent test collections based on Wikipedia paragraphs include rich text and entity representations [7]. As a result, this topic collection is an interesting domain for models that incorporate both text and entity representations of queries and documents [4], [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Local and Global Query Expansion for Hierarchical Complex Topics

Dalton

Naseri

Dietz

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Recent topic model extensions are either designed for specific tasks, such as multi-label classification (Li, Ouyang, and Zhou 2015a,b) and opinion mining (Wang, Chen, and Liu 2016), or particular kinds of texts, such as short texts (Zhang, Mao, and Zeng 2016;Bicalho et al 2017;Qiu and Shen 2017;Li et al 2018). On the other hand, the notion of entity salience is attracting more attention (Gamon et al 2013;Tran et al 2015;Escoter et al 2017;Xiong et al 2018). Gamon et al (2013) propose the task of identifying salient entities on web pages.…”

Section: Related Workmentioning

confidence: 99%

“…Escoter et al (2017) group business news stories based on the salience of named entities. Xiong et al (2018) propose a Kernel Entity Salience Model to better estimate entity salience in documents so as to improve text understanding and retrieval.…”

Section: Related Workmentioning

confidence: 99%

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It all starts with entities: A Salient entity topic model

Kanoulas

Rijke

2019

Nat. Lang. Eng.

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Entities play an essential role in understanding textual documents, regardless of whether the documents are short, such as tweets, or long, such as news articles. In short textual documents, all entities mentioned are usually considered equally important because of the limited amount of information. In long textual documents, however, not all entities are equally important: some are salient and others are not. Traditional entity topic models (ETMs) focus on ways to incorporate entity information into topic models to better explain the generative process of documents. However, entities are usually treated equally, without considering whether they are salient or not. In this work, we propose a novel ETM, Salient Entity Topic Model, to take salient entities into consideration in the document generation process. In particular, we model salient entities as a source of topics used to generate words in documents, in addition to the topic distribution of documents used in traditional topic models. Qualitative and quantitative analysis is performed on the proposed model. Application to entity salience detection demonstrates the effectiveness of our model compared to the state-of-the-art topic model baselines.

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Dataset or Not? A Study on the Veracity of Semantic Markup for Dataset Pages

Alrashed

Paparas

Benjelloun

et al. 2021

The Semantic Web – ISWC 2021

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Semantic markup, such as , allows providers on the Web to describe content using a shared controlled vocabulary. This markup is invaluable in enabling a broad range of applications, from vertical search engines, to rich snippets in search results, to actions on emails, to many others. In this paper, we focus on semantic markup for datasets, specifically in the context of developing a vertical search engine for datasets on the Web, Google’s Dataset Search. Dataset Search relies on to identify pages that describe datasets. While was the core enabling technology for this vertical search, we also discovered that we need to address the following problem: pages from 61% of internet hosts that provide markup do not actually describe datasets. We analyze the veracity of dataset markup for Dataset Search’s Web-scale corpus and categorize pages where this markup is not reliable. We then propose a way to drastically increase the quality of the dataset metadata corpus by developing a deep neural-network classifier that identifies whether or not a page with markup is a dataset page. Our classifier achieves 96.7% recall at the 95% precision point. This level of precision enables Dataset Search to circumvent the noise in semantic markup and to use the metadata to provide high quality results to users.

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Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

Cited by 34 publications

References 28 publications

Local and Global Query Expansion for Hierarchical Complex Topics

Local and Global Query Expansion for Hierarchical Complex Topics

It all starts with entities: A Salient entity topic model

Dataset or Not? A Study on the Veracity of Semantic Markup for Dataset Pages

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