A nonparametric model for online topic discovery with word embeddings

Chen, Junyang; Gong, Zhiguo; Liu, Weiwen

doi:10.1016/j.ins.2019.07.048

Cited by 54 publications

(15 citation statements)

References 10 publications

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“…Thereafter, MStreamF (Yin et al, 2018) was thus proposed by incorporating a forgetting mechanism to cope with cluster evolution, and allows processing each batch only one time. The NPMM model (Chen et al, 2019) was recently introduced by using the word-embeddings to eliminate a cluster generating parameter of the model.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the performance of the proposed algorithm, we conduct experiments on three real and two synthetic datasets. These datasets were also used in (Yin and Wang, 2016a;Qiang et al, 2018;Yin et al, 2018;Jia et al, 2018;Chen et al, 2019) to evaluate short text clustering models. In the preprocessing step, we removed stop words, converted all text into lowercase, and stemming.…”

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

“…Initially, traditional clustering algorithms for static data were enhanced and transformed for text streams (Zhong, 2005). Very soon, they are replaced by model-based algorithms such as LDA (Blei et al, 2003), DTM (Blei and Lafferty, 2006), TDPM (Ahmed and Xing, 2008), GSDMM (Yin and Wang, 2016b), DPMFP (Huang et al, 2013), TM-LDA (Wang et al, 2012), NPMM (Chen et al, 2019) and MStream (Yin et al, 2018), to mention a few. However, for most established approaches, they often work in a batch way, and assume the instances within a batch are interchangeable.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering

Kumar¹,

Shao²,

Uddin³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution. Existing approaches often exploit short text streams in a batch way. However, determine the optimal batch size is usually a difficult task since we have no prior knowledge when the topics evolve. In addition, traditional independent word representation in the graphical model tends to cause "term ambiguity" problem in short text clustering. Therefore, in this paper, we propose an Online Semantic-enhanced Dirichlet Model for short text stream clustering, called OSDM, which integrates the word-occurrence semantic information (i.e., context) into a new graphical model and clusters for each arriving short text automatically in an online way. Extensive results have demonstrated that OSDM gives better performance compared to many state-ofthe-art algorithms on both synthetic and realworld data sets.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering

Kumar¹,

Shao²,

Uddin³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

View full text Add to dashboard Cite

show abstract

“…Besides some other detection methods-including probability graph methods, deep learning methods, representation learning methods, and so on-are applied to perform event detection. For example, Chen et al 17 proposed a nonparametric model for online topic discovery with word embeddings, where they mainly exploited auxiliary word embeddings to infer the topic number and employed a ''spike and slab'' function to alleviate the sparsity problem of topic-word distributions in online short text analyses. Hu et al 18 presented a transformation-gated long short-term memory (LSTM) to enhance the ability of capturing short-term mutation information, where the function of transformation gate, input gate information, and the value range of the partial derivative corresponding to the transformation gate were studied.…”

Section: Related Workmentioning

confidence: 99%

A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

Wang

Lin³

et al. 2020

International Journal of Distributed Sensor Networks

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Quickly detecting related primitive events for multiple complex events from massive event stream usually faces with a great challenge due to their single pattern characteristic of the existing complex event detection methods. Aiming to solve the problem, a multiple pattern complex event detection scheme based on decomposition and merge sharing is proposed in this article. The achievement of this article lies that we successfully use decomposition and merge sharing technology to realize the high-efficient detection for multiple complex events from massive event streams. Specially, in our scheme, we first use decomposition sharing technology to decompose pattern expressions into multiple subexpressions, which can provide many sharing opportunities for subexpressions. We then use merge sharing technology to construct a multiple pattern complex events by merging sharing all the same prefix, suffix, or subpattern into one based on the above decomposition results. As a result, our proposed detection method in this article can effectively solve the above problem. The experimental results show that the proposed detection method in this article outperforms some general detection methods in detection model and detection algorithm in multiple pattern complex event detection as a whole.

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“…Subsequent works are mainly dedicated to designing elaborate features to improve detection performance. For example, textual features like psychological and linguistic clues [3], syntactic stylometry [4], review topic [5], [6], and behavioral features like rating deviation [7], [8] are explored in many works. However, designing effective features is usually time-consuming [9] and heavily rely on expert knowledge in particular areas.…”

Section: Introductionmentioning

confidence: 99%

Learning Review Representations from user and Product Level Information for Spam Detection

Yuan

Zhou

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

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Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame. Existing works mainly focus on manually designing discrete textual or behavior features, which cannot capture complex semantics of reviews. Although recent works apply deep learning methods to learn review-level semantic features, their models ignore the impact of the user-level and product-level information on learning review semantics and the inherent userreview-product relationship information.In this paper, we propose a Hierarchical Fusion Attention Network (HFAN) to automatically learn the semantics of reviews from user and product level. Specifically, we design a multiattention unit to extract user(product)-related review information. Then, we use orthogonal decomposition and fusion attention to learn a user, review, and product representation from the review information. Finally, we take the review as a relation between user and product entity and apply TransH to jointly encode this relationship into review representation. Experimental results obtained more than 10% absolute precision improvement over the state-of-the-art performances on four real-world datasets, which show the effectiveness and versatility of the model.Index Terms-Opinion mining, Opinion spam detection, Hierarchical fusion attention network, User-level and product-level information

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A nonparametric model for online topic discovery with word embeddings

Cited by 54 publications

References 10 publications

An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering

An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering

A multiple pattern complex event detection scheme based on decomposition and merge sharing for massive event streams

Learning Review Representations from user and Product Level Information for Spam Detection

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