Microblog Conversation Recommendation via Joint Modeling of Topics
            and Discourse

Zeng, Xingshan; Li, Jing; Wang, Lu; Beauchamp, Nicholas; Shugars, Sarah; Wong, Kam-Fai

doi:10.18653/v1/n18-1035

Cited by 33 publications

(47 citation statements)

References 42 publications

(34 reference statements)

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“…Topic Models. Well-known topic models, e.g., probabilistic latent semantic analysis (pLSA) (Hofmann, 1999) and latent Dirichlet allocation (LDA) (Blei et al, 2003), have shown advantages in capturing effective semantic representations, and proven beneficial to varying downstream applications, such as summarization (Haghighi and Vanderwende, 2009) and recommendation (Zeng et al, 2018;Bai et al, 2018). For short text data, topic model variants have been proposed to reduce the effects of sparsity issues on topic modeling, such as biterm topic model (BTM) (Yan et al, 2013) and LeadLDA (Li et al, 2016b).…”

Section: Related Workmentioning

confidence: 99%

Topic Memory Networks for Short Text Classification

Zeng¹,

Li²,

Yan³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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120

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Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics. * This work was mainly conducted when Jichuan Zeng was an intern in Tencent AI Lab. † Jing Li is the corresponding author. Training instances R1: [SuperBowl] I'll do anything to see the Steelers win. R2: [New.Music.Live] Please give wristbands, she have major Bieber Fever. Test instance S: [New.Music.Live] I will do anything for wristbands, gonna tweet till I win.

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Section: Related Workmentioning

confidence: 99%

Topic Memory Networks for Short Text Classification

Zeng¹,

Li²,

Yan³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Self Cite

120

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“…Both data sets were collected from Twitter. The Election-Trec data set is constructed based on the TREC2011 microblog track 1 and the Election data set 2 shared by Zeng et al (2018). For the TREC2011 data set, to recover conversations, we used the tweet search API 3 to retrieve full information of a tweet with its "in reply to status id" included.…”

Section: Data Collectionmentioning

confidence: 99%

Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction

Zhang

2019

Asso for Info Science & Tech

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Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word‐level indicative representation and message‐level indicative representation hierarchically. In both of the modules, we apply character‐level representations, which enables the model to explore morphological features and deal with the out‐of‐vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real‐life data sets indicate that our model outperforms state‐of‐the‐art models from previous studies.

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“…In previous studies, it has been shown that effective online conversation recommendation has the potential to produce more positive online social interaction experience (Chen et al, 2011;Zeng et al, 2018). Prior work on this subject has focused on post-level recommendation (Yan et al, 2012;Chen et al, 2012), or conversation-level suggestion with handcrafted features (Chen et al, 2011) and word co-occurrence patterns (Zeng et al, 2018). Nevertheless, they ignore the useful information embedded in replying relations, where the conversation structure is formed via messages sent among users.…”

Section: Introductionmentioning

confidence: 99%

“…We then incorporate the interaction representations into a novel neural collaborative filtering framework (He et al, 2017), which further aligns user's preferences with the conversation context. Compared with existing methods that are based on handcrafted features (Chen et al, 2011) or Bayesian models (Zeng et al, 2018), our end-toend trained neural model learns to automatically recommend conversations as well as to encode user interests embedded in their conversation interactions. To the best of our knowledge, this is the first work to explore neural conversation recommendation with online interactions explicitly encoded for user preference modeling.…”

Section: Introductionmentioning

confidence: 99%

Neural Conversation Recommendation with Online Interaction Modeling

Zeng

Wang

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis. It presents a concrete challenge for individuals to better discover and engage in social media discussions. In this paper, we present a novel framework to automatically recommend conversations to users based on their prior conversation behaviors. Built on neural collaborative filtering, our model explores deep semantic features that measure how a user's preferences match an ongoing conversation's context. Furthermore, to identify salient characteristics from interleaving user interactions, our model incorporates graph-structured networks, where both replying relations and temporal features are encoded as conversation context. Experimental results on two large-scale datasets collected from Twitter and Reddit show that our model yields better performance than previous stateof-the-art models, which only utilize lexical features and ignore past user interactions in the conversations.

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Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse

Cited by 33 publications

References 42 publications

Topic Memory Networks for Short Text Classification

Topic Memory Networks for Short Text Classification

Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction

Neural Conversation Recommendation with Online Interaction Modeling

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