Adapting Semantic Spreading Activation to Entity Linking in Text

Nooralahzadeh, Farhad; Lopez, Cédric; Cabrio, Elena; Gandon, Fabien; Segond, Frédérique

doi:10.1007/978-3-319-41754-7_7

Cited by 5 publications

(9 citation statements)

References 19 publications

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“…Various applications on knowledge bases build on such hierarchical structure in order to find semantic similarity between words, semantic relatedness, meaning, as well as for question answering. Among the main tools used in various such applications are label propagation (Quillian 1969) and spreading activation methods (Collins and Loftus 1975); e.g., (Salton and Buckley 1988;Harrington 2010;Nooralahzadeh et al 2016;Berger-Wolf et al 2013). Label propagation methods starting with two nodes having two distinct labels, proceed in iterations where a label is propagated to neighbors that obtained the label in the previous round.…”

Section: Discussionmentioning

confidence: 99%

Understanding the Semantic Content of Sparse Word Embeddings Using a Commonsense Knowledge Base

Balogh

Berend

Diochnos

et al. 2020

AAAI

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Word embeddings have developed into a major NLP tool with broad applicability. Understanding the semantic content of word embeddings remains an important challenge for additional applications. One aspect of this issue is to explore the interpretability of word embeddings. Sparse word embeddings have been proposed as models with improved interpretability. Continuing this line of research, we investigate the extent to which human interpretable semantic concepts emerge along the bases of sparse word representations. In order to have a broad framework for evaluation, we consider three general approaches for constructing sparse word representations, which are then evaluated in multiple ways. We propose a novel methodology to evaluate the semantic content of word embeddings using a commonsense knowledge base, applied here to the sparse case. This methodology is illustrated by two techniques using the ConceptNet knowledge base. The first approach assigns a commonsense concept label to the individual dimensions of the embedding space. The second approach uses a metric, derived by spreading activation, to quantify the coherence of coordinates along the individual axes. We also provide results on the relationship between the two approaches. The results show, for example, that in the individual dimensions of sparse word embeddings, words having high coefficients are more semantically related in terms of path lengths in the knowledge base than the ones having zero coefficients.

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Section: Discussionmentioning

confidence: 99%

Understanding the Semantic Content of Sparse Word Embeddings Using a Commonsense Knowledge Base

Balogh

Berend

Diochnos

et al. 2020

AAAI

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“…BBC, CNN, CNBC). If no matches are found, the algorithm extracts the NEs from the tweets through (Nooralahzadeh et al, 2016)'s system, and applies the following two heuristics: i) if a NE is of type dbo:Organisation or dbo:Person, it considers such NE as the source; ii) it searches in the abstract of the DBpedia 7 page linked to that NE if the words "news", "newspaper" or "magazine" appear (if found, such entity is considered as the source). In the example above, the following NEs have been detected in the tweet: "The Guardian" (linked to the DBpedia resource http://dbpedia.org/page/ The_Guardian) and "Greek crisis" (linked to http://dbpedia.org/page/Greek_ government-debt_crisis).…”

Section: Task 3: Source Identificationmentioning

confidence: 99%

Argument Mining on Twitter: Arguments, Facts and Sources

Dusmanu¹,

Cabrio²,

Villata

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Social media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in. Applying argument mining methods to such heterogeneous data sources is a challenging open research issue, in particular considering the peculiarities of the language used to write textual messages on social media. In addition, new issues emerge when dealing with arguments posted on such platforms, such as the need to make a distinction between personal opinions and actual facts, and to detect the source disseminating information about such facts to allow for provenance verification. In this paper, we apply supervised classification to identify arguments on Twitter, and we present two new tasks for argument mining, namely facts recognition and source identification. We study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexit news topics.

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“…By using this dataset, the results of our system in the task of entity linking can be compared directly with other systems that have been tested with the same public dataset. We extract the comparison data from [26]. These results can be observed in Table I, in terms of precision (P%), recall (R%), and Fmeasure (F1%).…”

Section: A Oke Evaluation Datasetmentioning

confidence: 99%

NEREA: Named Entity Recognition and Disambiguation Exploiting Local Document Repositories

Garrido

Ilarri

Sangiao³

et al. 2016

2016 IEEE 28th International Conference on Tools With Artificial Intelligence (ICTAI)

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In this work, we describe the design, development, and deployment of NEREA (Named Entity Recognizer for spEcific Areas), an automatic Named Entity Recognizer and Disambiguation system, developed in collaboration with professional documentalists. The aim of NEREA is to keep accurate and current information about the entities mentioned in a local repository, and then support building appropriate infoboxes, setting out the main data of these entities. It achieves a high performance thanks to the use of classification resources belonging to the local database. With this aim, the system performs tasks of named entity recognition and disambiguation by using three types of knowledge bases: local classification resources, global databases like DBpedia, and its own catalog created by NEREA. The proposed method has been validated with two different datasets and its operation has been tested in English and Spanish. The working methodology is being applied in a real environment of a media with promising results.

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Adapting Semantic Spreading Activation to Entity Linking in Text

Cited by 5 publications

References 19 publications

Understanding the Semantic Content of Sparse Word Embeddings Using a Commonsense Knowledge Base

Understanding the Semantic Content of Sparse Word Embeddings Using a Commonsense Knowledge Base

Argument Mining on Twitter: Arguments, Facts and Sources

NEREA: Named Entity Recognition and Disambiguation Exploiting Local Document Repositories

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