An N-Gram Topic Model for Time-Stamped Documents

Jameel, Shoaib; Lam, Wai

doi:10.1007/978-3-642-36973-5_25

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…It also differs from our new models proposed in this paper as we have incorporated side-information, where our previous model is unsupervised. Our temporal model proposed in Jameel and Lam (2013c), also generates more interpretable latent topics with word order. However, this model does not consider side-information and cannot solve document retrieval learning task.…”

Section: Our Previous Workmentioning

confidence: 99%

Supervised topic models with word order structure for document classification and retrieval learning

Jameel

Lam

2015

Inf Retrieval J

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One limitation of most existing probabilistic latent topic models for document classification is that the topic model itself does not consider useful side-information, namely, class labels of documents. Topic models, which in turn consider the side-information, popularly known as supervised topic models, do not consider the word order structure in documents. One of the motivations behind considering the word order structure is to capture the semantic fabric of the document. We investigate a low-dimensional latent topic model for document classification. Class label information and word order structure are integrated into a supervised topic model enabling a more effective interaction among such information for solving document classification. We derive a collapsed Gibbs sampler for our model. Likewise, supervised topic models with word order structure have not been explored in document retrieval learning. We propose a novel supervised topic model for document retrieval learning which can be regarded as a pointwise model for tackling the learning-to-rank task. Available relevance assessments and word order structure are integrated into the topic model itself. We conduct extensive experiments on several publicly available benchmark datasets, and show that our model improves upon the state-of-the-art models.

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Section: Our Previous Workmentioning

confidence: 99%

Supervised topic models with word order structure for document classification and retrieval learning

Jameel

Lam

2015

Inf Retrieval J

Self Cite

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“…Later work followed that proposed extensions to identifying topical phrases [8,20,3]. Work by Jameel et al in 2013 [7] combined n-gram models with temporal documents and was foundational in using ontological concepts to ground the topic modeling process.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Grounded Topic Modeling for Climate Science Research

Sleeman¹,

Finin²,

Halem³

2018

Preprint

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In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable. Topic modeling is an effective technique for summarizing a collection of documents to find the main themes among them and to classify other documents that have a similar mixture of co-occurring words. We show how grounding a topic model with an ontology, extracted from a glossary of important domain phrases, improves the topics generated and makes them easier to understand. We apply and evaluate this method to the climate science domain. The result improves the topics generated and supports faster research understanding, discovery of social networks among researchers, and automatic ontology generation.

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“…Subsquent models have sought to encourage topically coherent collocations, including Phrase-Discovering LDA (Lindsey et al, 2012), the timebased topical n-gram model (Jameel and Lam, 2013a) and the n-gram Hierarchical Dirichlet Process (HDP) model (Jameel and Lam, 2013b). Phrase-Discovering LDA is a non-parametric ex-tension of TNG inspired by Bayesian N-gram models Teh (2006) that incorporate a Pitman-Yor Process prior.…”

Section: Related Workmentioning

confidence: 99%

A Computationally Efficient Algorithm for Learning Topical Collocation Models

Zhao¹,

Du²,

Börschinger

et al. 2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

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Most existing topic models make the bagof-words assumption that words are generated independently, and so ignore potentially useful information about word order. Previous attempts to use collocations (short sequences of adjacent words) in topic models have either relied on a pipeline approach, restricted attention to bigrams, or resulted in models whose inference does not scale to large corpora. This paper studies how to simultaneously learn both collocations and their topic assignments. We present an efficient reformulation of the Adaptor Grammar-based topical collocation model (AG-colloc) (Johnson, 2010), and develop a point-wise sampling algorithm for posterior inference in this new formulation. We further improve the efficiency of the sampling algorithm by exploiting sparsity and parallelising inference. Experimental results derived in text classification, information retrieval and human evaluation tasks across a range of datasets show that this reformulation scales to hundreds of thousands of documents while maintaining the good performance of the AG-colloc model.

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An N-Gram Topic Model for Time-Stamped Documents

Cited by 7 publications

References 18 publications

Supervised topic models with word order structure for document classification and retrieval learning

Supervised topic models with word order structure for document classification and retrieval learning

Ontology-Grounded Topic Modeling for Climate Science Research

A Computationally Efficient Algorithm for Learning Topical Collocation Models

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