The Role of Query Sessions in Interpreting Compound Noun Phrases

Paşca, Marius

doi:10.1145/2806416.2806571

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…The methodologically closest work to ours is [23]. Like us, that work used interrogative patterns (e.g.…”

Section: Related Workmentioning

confidence: 92%

“…One can think of this as organizing CSK in SP pairs where P is a textual phrase that comprises both predicate and object but cannot separate these two. Moreover, [23] restricted itself to the extraction stage and used simple scoring from query frequencies, whereas we go further by leveraging multi-source signals in the corroboration stage and refining the SPO assertions into semantic groups.…”

Section: Cycmentioning

confidence: 99%

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Commonsense Properties from Query Logs and Question Answering Forums

Romero

Razniewski

Pal

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality.

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“…The methodologically closest work to ours is [23]. Like us, that work used interrogative patterns (e.g.…”

Section: Related Workmentioning

confidence: 92%

Section: Cycmentioning

confidence: 99%

Commonsense Properties from Query Logs and Question Answering Forums

Romero

Razniewski

Pal

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

show abstract

“…Tapping Query Logs: Similar principles of distant supervision and the learning of hyper-patterns that generalize from specified properties to a-priori unknown properties, also apply to query logs as an input source [443,201,442,347]. For example, many users ask search engines about "sales of album ", "lyrics writer of song ", "topic of song ", "meaning of song ", "what is song about?…”

Section: Distant Supervision For Open Ie From Textmentioning

confidence: 99%

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Weikum¹,

Dong²,

Razniewski³

et al. 2020

Preprint

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Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics.This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

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“…The automatic interpretation models of Nominal Compounds mainly apply three strategies: top-down strategy (Levin, 1978; Zhao et al , 2007; Su and Baldwin, 2006; Li, 2009; Butnariu and Veale, 2008), bottom-up strategy (Nakov and Hearst, 2006; Nakov, 2008; Li et al , 2010; Wang et al , 2010, 2016; Pasca, 2015a, 2015b) and top-down and bottom-up strategy (Wei and Yuan, 2014).…”

Section: Introductionmentioning

confidence: 99%

An effective approach for automatic interpretation of Chinese nominal compounds

Dai

et al. 2017

IDD

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Purpose Automatic interpretation of Nominal Compounds is a crucial issue for many applications, for example, sentence understanding, machine translation, question-answering system and so forth. Many automatic interpretation models of Nominal Compounds use the strategies based on verbs or rules to obtain the interpretation of compounds. However, the performances of these models are still limited. The purpose of this paper is to propose an effective approach for automatic interpretation of Chinese nominal compounds. Design/methodology/approach The authors propose a top-down and bottom-up model based on rules and large-scale corpus for automatic interpretation of Nominal Compounds. Findings Experimental results demonstrate that the proposed model outperforms the state-of-the-art automatic interpretation model. Originality/value The paper is an up-to-date study of automatic interpretation for Nominal Compounds. It can help people understand the meaning of Nominal Compounds in reading. With a better understanding of Nominal Compounds, we can discover more hidden knowledge in them.

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The Role of Query Sessions in Interpreting Compound Noun Phrases

Cited by 4 publications

References 35 publications

Commonsense Properties from Query Logs and Question Answering Forums

Commonsense Properties from Query Logs and Question Answering Forums

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

An effective approach for automatic interpretation of Chinese nominal compounds

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