Using Aspect Extraction Approaches to Generate Review Summaries and
            User Profiles

Mitcheltree, Christopher; Wharton, Veronica; Saluja, Avneesh

doi:10.18653/v1/n18-3009

Cited by 7 publications

(5 citation statements)

References 9 publications

(10 reference statements)

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“…It thus acts as an 'interpretability layer' that can be applied over the scene encoder. This model is similar in spirit to dynamic topic models (Blei and Lafferty, 2006), with the added advantage of producing topics that are both more coherent and more interpretable than those generated by LDA (He et al, 2017;Mitcheltree et al, 2018).…”

Section: Interpreting Scene Embeddingsmentioning

confidence: 99%

Hierarchical Encoders for Modeling and Interpreting Screenplays

Bhat¹,

Saluja²,

Dye³

et al. 2021

Proceedings of the Third Workshop on Narrative Understanding

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While natural language understanding of longform documents remains an open challenge, such documents often contain structural information that can inform the design of models encoding them. Movie scripts are an example of such richly structured text -scripts are segmented into scenes, which decompose into dialogue and descriptive components. In this work, we propose a neural architecture to encode this structure, which performs robustly on two multi-label tag classification tasks without using handcrafted features. We add a layer of insight by augmenting the encoder with an unsupervised 'interpretability' module, which can be used to extract and visualize narrative trajectories. Though this work specifically tackles screenplays, we discuss how the underlying approach can be generalized to a range of structured documents.

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Section: Interpreting Scene Embeddingsmentioning

confidence: 99%

Hierarchical Encoders for Modeling and Interpreting Screenplays

Bhat¹,

Saluja²,

Dye³

et al. 2021

Proceedings of the Third Workshop on Narrative Understanding

Self Cite

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“…In this work, we concentrate on the ABAE model [8]. Since it was put forward in 2017, recent studies have utilized ABAE for various NLP tasks including rating prediction [22] and user profiling [19]. Unsupervised aspect extraction models such as ABAE [8] are shown to yield interpretable and coherent aspects for the reviews of various goods (usually tested on the Amazon reviews dataset), which are typically short and very focused on certain items of interest of the reviewer.…”

Section: Related Workmentioning

confidence: 99%

“…Unsupervised aspect extraction models such as ABAE [8] are shown to yield interpretable and coherent aspects for the reviews of various goods (usually tested on the Amazon reviews dataset), which are typically short and very focused on certain items of interest of the reviewer. Researchers from the Airbnb team applied ABAE to a large corpus of accommodation reviews in order to generate review summaries and user profiles [19]. They evaluated ABAE across these two tasks.…”

Section: Related Workmentioning

confidence: 99%

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Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification

Alekseev

Tutubalina

Malykh

et al. 2020

IFS

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Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling. While models such as neural attention-based aspect extraction (ABAE) have been successfully applied to user-generated texts, they are less coherent when applied to traditional data sources such as news articles and newsgroup documents. In this work, we introduce a simple approach based on sentence filtering in order to improve topical aspects learned from newsgroups-based content without modifying the basic mechanism of ABAE. We train a probabilistic classifier to distinguish between out-of-domain texts (outer dataset) and in-domain texts (target dataset). Then, during data preparation we filter out sentences that have a low probability of being in-domain and train the neural model on the remaining sentences. The positive effect of sentence filtering on topic coherence is demonstrated in comparison to aspect extraction models trained on unfiltered texts.

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“…As the scale of online services and the Web itself grows, recommender systems increasingly attempt to utilize texts available online, either as items for recommendation or as their descriptions [1,24,27,43]. One key complication is that a single text can touch upon many different features of the item; e.g., the same brief review of a laptop can assess its weight, performance, keyboard, and so on, with different results.…”

Section: Introductionmentioning

confidence: 99%

AspeRa: Aspect-Based Rating Prediction Model

Nikolenko

Tutubalina

Malykh

et al. 2019

Lecture Notes in Computer Science

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We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users. The AspeRa model uses max-margin losses for joint item and user embedding learning and a dual-headed architecture; it significantly outperforms recently proposed state-of-the-art models such as DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews. With qualitative examination of the aspects and quantitative evaluation of rating prediction models based on these aspects, we show how aspect embeddings can be used in a recommender system.Keywords: aspect-based sentiment analysis · recommender systems · aspect-based recommendation · explainable recommendation · user reviews · neural network · deep learning

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Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles

Cited by 7 publications

References 9 publications

Hierarchical Encoders for Modeling and Interpreting Screenplays

Hierarchical Encoders for Modeling and Interpreting Screenplays

Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification

AspeRa: Aspect-Based Rating Prediction Model

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