BL-LDA: Bringing Bigram to Supervised Topic Model

Park, Youngsun; Alam, Md. Hijbul; Ryu, Woo-Jong; Lee, Sangkeun

doi:10.1109/csci.2015.146

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In this paper, [1] propose to enhance topic modeling by enable a system to dynamically search for word concurrence text that are significant to identifying a target word. Phrase-based system using this new strategy BL LDA incorporates the bigram concept for supervised generative modeling for multilabel text input.…”

Section: Literature Surveymentioning

confidence: 99%

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Information Retrieval Using Effective Bigram Topic Modeling

Chakkarwar¹,

Tamane²

2023

Advances in Computer Science Research

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Many fields, such as film reviews, recommendation systems, and language processing, have effectively adopted and utilized topic modeling with Latent Dirichlet Allocation (LDA). Many texts analysis tasks, however, rely heavily on sentence construction and words to capture the meaning of text. However, word coexistence plays an important role in retrieving significant data. In this paper we present a novel method which discovers topics and topical phrases using language modeling. Proposed bigram extended LDA gives promising results to discover latent research areas in research articles and efficient classification of research articles. Experimental results are carried out to test the efficiency of proposed method.

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Section: Literature Surveymentioning

confidence: 99%

“…A quality term in the topic of interest indicates a notion, idea, approach, or procedure. A number of applications require the mining of high-quality phrases [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Information Retrieval Using Effective Bigram Topic Modeling

Chakkarwar¹,

Tamane²

2023

Advances in Computer Science Research

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“…This process becomes complex when interpreting the relations between words, because such models do not capture the different meanings of words in context (polysemy). Due to the problems associated with LDA, we resorted to Bringing Bigram to Supervised Topic Model (BL-LDA) [32], which is a supervised generative model for multi-labeled text, that actually extends LDA by applying the bigram concept. Accordingly, this approach can be used as proposed in Algorithm 4.…”

Section: Linguistic Detectionmentioning

confidence: 99%

Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities

et al. 2021

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The emergence of digital social networks has transformed society, social groups, and institutions in terms of the communication and expression of their opinions. Determining how language variations allow the detection of communities, together with the relevance of specific vocabulary (proposed by the National Council of Accreditation of Colombia (Consejo Nacional de Acreditación -CNA) to determine the quality evaluation parameters for universities in Colombia) in digital assemblages could lead to a better understanding of their dynamics and social foundations, thus resulting in better communication policies and intervention where necessary. The approach presented in this paper intends to determine what are the semantic spaces (sociolinguistic features) shared by social groups in digital social networks. It includes five layers based on Design Science Research, which are integrated with Natural Language Processing techniques (NLP), Computational Linguistics (CL), and Artificial Intelligence (AI). The approach is validated through a case study wherein the semantic values of a series of "Twitter" institutional accounts belonging to Colombian Universities are analyzed in terms of the 12 quality factors established by CNA. In addition, the topics and the sociolect used by different actors in the university communities are also analyzed. The current approach allows determining the sociolinguistic features of social groups in digital social networks. Its application allows detecting the words or concepts to which each actor of a social group (university) gives more importance in terms of vocabulary.

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Semantic relation extraction aware of N-gram features from unstructured biomedical text

Wang

Zhu

2018

Journal of Biomedical Informatics

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Semantic relation extraction is a crucial step of automatically constructing a knowledge graph from unstructured biomedical text. Many real-world applications can benefit from it. As unsupervised relation extraction approaches, generative probabilistic models, Rel-LDA and Type-LDA, are receiving more attention in recent years. However, these two models inherit the bag-of-word assumption of the standard LDA model, which disable the exploitation of more distinguishable n-gram features. To overcome this limitation, two alternative models, named as Rel-TNG and Type-TNG, are proposed with the help of Topic N-Grams (TNG) model in this study, and collapsed Gibbs sampling algorithm is utilized for inference. Extensive experimental results on GENIA and EPI corpora indicate that Rel-TNG and Type-TNG models have similar performance with their unigram counterparts, but Rel-TNG and Type-TNG models outperform Rel-LDA and Type-LDA models when prior knowledge is available.

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BL-LDA: Bringing Bigram to Supervised Topic Model

Cited by 3 publications

References 8 publications

Information Retrieval Using Effective Bigram Topic Modeling

Information Retrieval Using Effective Bigram Topic Modeling

Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities

Semantic relation extraction aware of N-gram features from unstructured biomedical text

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