Erd'14

Carmel, David; Chang, Ming‐Wei; Gabrilovich, Evgeniy; Hsu, Bo-June; Wang, Kuansan

doi:10.1145/2600428.2600734

Cited by 50 publications

(2 citation statements)

References 22 publications

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“…We use precision, recall and F1 scores to evaluate and compare the approaches. We follow Carmel et al (2014) and Yang and Chang (2015) and define the scores on a per-entity basis. Since there are no mention boundaries for the gold entities, an extracted entity is considered correct if it is present in the set of the gold entities for the given question.…”

Section: Evaluation Methodologymentioning

confidence: 99%

Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories

Sorokin¹,

Gurevych²

2018

Proceedings of the Seventh Joint Conference on Lexical And Computational Semantics

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The first stage of every knowledge base question answering approach is to link entities in the input question. We investigate entity linking in the context of a question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity.We use the Wikidata knowledge base and available question answering datasets to create benchmarks for entity linking on question answering data. Our approach outperforms the previous state-of-the-art system on this data, resulting in an average 8% improvement of the final score. We further demonstrate that our model delivers a strong performance across different entity categories.

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Section: Evaluation Methodologymentioning

confidence: 99%

Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories

Sorokin¹,

Gurevych²

2018

Proceedings of the Seventh Joint Conference on Lexical And Computational Semantics

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“…As schematized in Figure 1, these entities and their relations are represented in a graph structure as the nodes and edges, respectively, leading to the name of MAG. Note that the entity recognition and disambiguation (ERD), as reported in (Carmel et al, 2014), is far from a solved problem. However, the key here is the AI technologies employed in MAS are designed to learn and improve by itself by repeatedly reading more materials than any human can possibly do in a lifetime.…”

Section: Introductionmentioning

confidence: 99%

A Review of Microsoft Academic Services for Science of Science Studies

et al. 2019

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Since the relaunch of Microsoft Academic Services (MAS) 4 years ago, scholarly communications have undergone dramatic changes: more ideas are being exchanged online, more authors are sharing their data, and more software tools used to make discoveries and reproduce the results are being distributed openly. The sheer amount of information available is overwhelming for individual humans to keep up and digest. In the meantime, artificial intelligence (AI) technologies have made great strides and the cost of computing has plummeted to the extent that it has become practical to employ intelligent agents to comprehensively collect and analyze scholarly communications. MAS is one such effort and this paper describes its recent progresses since the last disclosure. As there are plenty of independent studies affirming the effectiveness of MAS, this paper focuses on the use of three key AI technologies that underlies its prowess in capturing scholarly communications with adequate quality and broad coverage: (1) natural language understanding in extracting factoids from individual articles at the web scale, (2) knowledge assisted inference and reasoning in assembling the factoids into a knowledge graph, and (3) a reinforcement learning approach to assessing scholarly importance for entities participating in scholarly communications, called the saliency, that serves both as an analytic and a predictive metric in MAS. These elements enhance the capabilities of MAS in supporting the studies of science of science based on the GOTO principle, i.e., good and open data with transparent and objective methodologies. The current direction of development and how to access the regularly updated data and tools from MAS, including the knowledge graph, a REST API and a website, are also described.

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