Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability
Digital humanities scholars strongly need a corpus exploration method that provides topics easier to interpret than standard LDA topic models. To move towards this goal, here we propose a combination of two techniques, called Entity Linking and Labeled LDA. Our method identifies in an ontology a ser...
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Accademia University Press
2016
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oai:doaj.org-article:462f09ce7b074635867fb54bdb8646c02021-12-02T09:52:27ZEntities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability2499-455310.4000/ijcol.392https://doaj.org/article/462f09ce7b074635867fb54bdb8646c02016-12-01T00:00:00Zhttp://journals.openedition.org/ijcol/392https://doaj.org/toc/2499-4553Digital humanities scholars strongly need a corpus exploration method that provides topics easier to interpret than standard LDA topic models. To move towards this goal, here we propose a combination of two techniques, called Entity Linking and Labeled LDA. Our method identifies in an ontology a series of descriptive labels for each document in a corpus. Then it generates a specific topic for each label. Having a direct relation between topics and labels makes interpretation easier; using an ontology as background knowledge limits label ambiguity. As our topics are described with a limited number of clear-cut labels, they promote interpretability and support the quantitative evaluation of the obtained results. We illustrate the potential of the approach by applying it to three datasets, namely the transcription of speeches from the European Parliament fifth mandate, the Enron Corpus and the Hillary Clinton Email Dataset. While some of these resources have already been adopted by the natural language processing community, they still hold a large potential for humanities scholars, part of which could be exploited in studies that will adopt the fine-grained exploration method presented in this paper.Anne LauscherPablo Ruiz FaboFederico NanniSimone Paolo PonzettoAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 2, Iss 2, Pp 67-87 (2016) |
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DOAJ |
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EN |
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Social Sciences H Computational linguistics. Natural language processing P98-98.5 |
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Social Sciences H Computational linguistics. Natural language processing P98-98.5 Anne Lauscher Pablo Ruiz Fabo Federico Nanni Simone Paolo Ponzetto Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability |
| description |
Digital humanities scholars strongly need a corpus exploration method that provides topics easier to interpret than standard LDA topic models. To move towards this goal, here we propose a combination of two techniques, called Entity Linking and Labeled LDA. Our method identifies in an ontology a series of descriptive labels for each document in a corpus. Then it generates a specific topic for each label. Having a direct relation between topics and labels makes interpretation easier; using an ontology as background knowledge limits label ambiguity. As our topics are described with a limited number of clear-cut labels, they promote interpretability and support the quantitative evaluation of the obtained results. We illustrate the potential of the approach by applying it to three datasets, namely the transcription of speeches from the European Parliament fifth mandate, the Enron Corpus and the Hillary Clinton Email Dataset. While some of these resources have already been adopted by the natural language processing community, they still hold a large potential for humanities scholars, part of which could be exploited in studies that will adopt the fine-grained exploration method presented in this paper. |
| format |
article |
| author |
Anne Lauscher Pablo Ruiz Fabo Federico Nanni Simone Paolo Ponzetto |
| author_facet |
Anne Lauscher Pablo Ruiz Fabo Federico Nanni Simone Paolo Ponzetto |
| author_sort |
Anne Lauscher |
| title |
Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability |
| title_short |
Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability |
| title_full |
Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability |
| title_fullStr |
Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability |
| title_full_unstemmed |
Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability |
| title_sort |
entities as topic labels: combining entity linking and labeled lda to improve topic interpretability and evaluability |
| publisher |
Accademia University Press |
| publishDate |
2016 |
| url |
https://doaj.org/article/462f09ce7b074635867fb54bdb8646c0 |
| work_keys_str_mv |
AT annelauscher entitiesastopiclabelscombiningentitylinkingandlabeledldatoimprovetopicinterpretabilityandevaluability AT pabloruizfabo entitiesastopiclabelscombiningentitylinkingandlabeledldatoimprovetopicinterpretabilityandevaluability AT federiconanni entitiesastopiclabelscombiningentitylinkingandlabeledldatoimprovetopicinterpretabilityandevaluability AT simonepaoloponzetto entitiesastopiclabelscombiningentitylinkingandlabeledldatoimprovetopicinterpretabilityandevaluability |
| _version_ |
1718397940998864896 |