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

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1873–1883 Copenhagen, Denmark, September 7–11, 2017. c⃝2017 Association for Computational Linguistics Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels Lea Frermann University of Edinburgh ∗ l.frermann@ed.ac.uk Gy¨orgy Szarvas Amazon Development Center Germany GmbH szarvasg@amazon.de Abstract Automatically understanding the plot of novels is important both for informing lit- erary scholarship and applications such as summarization or recommendation. Vari- ous models have addressed this task, but their evaluation has remained largely in- trinsic and qualitative. Here, we pro- pose a principled and scalable framework leveraging expert-provided semantic tags (e.g., mystery, pirates) to evaluate plot rep- resentations in an extrinsic fashion, assess- ing their ability to produce locally coher- ent groupings of novels (micro-clusters) in model space. We present a deep recur- rent autoencoder model that learns richly structured multi-view plot representations, and show that they i) yield better micro- clusters than less structured representa- tions; and ii) are interpretable, and thus useful for further literary analysis or la- belling of the emerging micro-clusters. 1 Introduction For the literature aficionado, the quest for the next novel to read can be daunting: the sheer number of novels of different styles, topics and genres is dif- ficult to navigate. It is intuitively clear that readers select novels based on specific but potentially di- verse and structured preferences (e.g., they might prefer novels of a particular theme (small-town ro- mance), mood (dark) or based on character types (grumpy boss), character relations (love, enmity) and their development). These preferences also manifest in the organization of online book stores or recommendation platforms.1 For example, the ∗ Work done while the first author was an intern at Ama- zon (ADC Germany GmbH, Berlin). 1E.g., www.amazon.com or www.goodreads.com Amazon book catalog contains semantic tags pro- vided by experts (publishers), including labels of character types (pirates) or theme (secret baby ro- mance) to aid focused search for novels of interest. Although these tags are already fairly granular, many cover large sets of novels (e.g., the tag secret baby romance covers almost 4, 000 novels), limit- ing their utility for exhaustive exploration and call for even finer grained micro-groupings. Can we instead automatically induce fine-grained novel clusters in an unsupervised, data-driven way? We propose a framework to learn structured, in- terpretable book representations that capture dif- ferent aspects of the plot, and verify that such representations are rich enough to support down- stream tasks like generating interpretable book groupings. A real-world application of this work is content-based book recommendation based on diverse and interpretable book characteristics. Content-based recommendation has been criti- cized by the limited complexity of typically em- ployed features (limited content analysis; Lops et al. (2011); Adomavicius and Tuzhilin (2005)). This work addresses this problem by inducing complex, structured and interpretable representa- tions. Our contributions are two-fold. First, assuming that richly structured book tags call for rich content representations (which expert taggers arguably possess), we describe a deep un- supervised model for learning multi-view repre- sentations …

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