Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels

Lea Frermann, Gyuri Szarvas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automatically understanding the plot of novels is important both for informing literary scholarship and applications such as summarization or recommendation. Various models have addressed this task, but their evaluation has remained largely intrinsic and qualitative. Here, we propose a principled and scalable framework leveraging expert-provided semantic tags (e.g., mystery, pirates) to evaluate plot representations in an extrinsic fashion, assessing their ability to produce locally coherent groupings of novels (micro-clusters) in model space. We present a deep recurrent autoencoder model that learns richly structured multi-view plot representations, and show that they i) yield better microclusters than less structured representations; and ii) are interpretable, and thus useful for further literary analysis or labelling of the emerging micro-clusters.
Original languageEnglish
Title of host publicationProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)
PublisherAssociation for Computational Linguistics
Pages1873–1883
Number of pages11
DOIs
Publication statusPublished - 7 Sep 2017
EventEMNLP 2017: Conference on Empirical Methods in Natural Language Processing - Copenhagen, Denmark
Duration: 7 Sep 201711 Sep 2017
http://emnlp2017.net/index.html
http://emnlp2017.net/

Conference

ConferenceEMNLP 2017: Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2017
CountryDenmark
CityCopenhagen
Period7/09/1711/09/17
Internet address

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