Word Embeddings for Unsupervised Named Entity Linking

Nozza, Debora; Sas, Cezar; Fersini, Elisabetta; Messina, Enza

doi:10.1007/978-3-030-29563-9_13

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Once the named entities are recognized, the next step is to associate them to unambigous concepts, as for example resources in a Knowledge Base. This process is known as the task of Named Entity Linking (NEL) (Cucerzan, 2007;Dredze et al, 2010;Basile et al, 2015;Cecchini et al, 2016;Nozza et al, 2019).…”

Section: Experimental Settingmentioning

confidence: 99%

Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models

Terragni¹,

Nozza²,

Fersini³

et al. 2020

Proceedings of the First Workshop on Insights From Negative Results in NLP

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Topic models have been widely used to discover hidden topics in a collection of documents. In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships. While exploiting the document network significantly improves topic coherence, the introduction of concepts and their relationships does not influence the results both quantitatively and qualitatively.

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Section: Experimental Settingmentioning

confidence: 99%

Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models

Terragni¹,

Nozza²,

Fersini³

et al. 2020

Proceedings of the First Workshop on Insights From Negative Results in NLP

Self Cite

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RSVN: A RoBERTa Sentence Vector Normalization Scheme for Short Texts to Extract Semantic Information

Gao

Zhang

et al. 2022

Applied Sciences

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With the explosive growth in short texts on the Web and an increasing number of Web corpora consisting of short texts, short texts are playing an important role in various Web applications. Entity linking is a crucial task in knowledge graphs and a key technology in the field of short texts that affects the accuracy of many downstream tasks in natural language processing. However, compared to long texts, the entity-linking task of Chinese short text is a challenging problem due to the serious colloquialism and insufficient contexts. Moreover, existing methods for entity linking in Chinese short text underutilize semantic information and ignore the interaction between label information and the original short text. In this paper, we propose a RoBERTa sentence vector normalization scheme for short texts to fully extract the semantic information. Firstly, the proposed model utilizes RoBERTa to fully capture contextual semantic information. Secondly, the anisotropy of RoBERTa’s output sentence vectors is revised by utilizing the standard Gaussian of flow model, which enables the sentence vectors to more precisely characterize the semantics. In addition, the interaction between label embedding and text embedding is employed to improve the NIL entity classification. Experimental results demonstrate that the proposed model outperforms existing research results and mainstream deep learning methods for entity linking in two Chinese short text datasets.

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Что Имеет Большее Значение? Сравнение Влияния Концептуальных И Документальных Отношений В Тематических Моделях

ТЕРРАНЬИ¹,

ФЕРСИНИ²,

МЕССИНА³

et al. 2021

Международный Форум По Информации

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Тематические модели широко используются для обнаружения скрытых тем в коллекциях документов. В статье предполагается изучить роль двух разных типов реляционной информации, т.е. документальных отношений и концептуальных отношений. Хотя использование сети документов значительно улучшает когерентность темы, введение понятий и их отношений не оказывает влияния на результаты как качественно, так и количественно.

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Word Embeddings for Unsupervised Named Entity Linking

Cited by 3 publications

References 21 publications

Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models

Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models

RSVN: A RoBERTa Sentence Vector Normalization Scheme for Short Texts to Extract Semantic Information

Что Имеет Большее Значение? Сравнение Влияния Концептуальных И Документальных Отношений В Тематических Моделях

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