Text Categorization Based on Topic Model

Zhou, Shibin; Li, Kan; Liu, Yushu

doi:10.1080/18756891.2009.9727671

Cited by 23 publications

(5 citation statements)

References 5 publications

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“…In order to operationalize the evaluation of selection bias, we use topic models to capture latent semantics. Regularly used topic modeling techniques such as Latent Dirichlet Allocation (LDA) (Blei et al, 2003) have proven their efficiency to handle several NLP applications such as data exploration (Rodriguez and Storer, 2020), Twitter hashtag recommendation (Godin et al, 2013), authorship attribution (Seroussi et al, 2014), and text categorization (Zhou et al, 2009). In order to evaluate the consistency of the generated topics, Newman et al (2010) Lau and Baldwin (2016) investigated the effect of cardinality on topic generation.…”

Section: Related Workmentioning

confidence: 99%

Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets

Ousidhoum¹,

Song²,

Yeung³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Work on bias in hate speech typically aims to improve classification performance while relatively overlooking the quality of the data. We examine selection bias in hate speech in a language and label independent fashion. We first use topic models to discover latent semantics in eleven hate speech corpora, then, we present two bias evaluation metrics based on the semantic similarity between topics and search words frequently used to build corpora. We discuss the possibility of revising the data collection process by comparing datasets and analyzing contrastive case studies.

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Section: Related Workmentioning

confidence: 99%

Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets

Ousidhoum¹,

Song²,

Yeung³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…Apart from the mentioned studies, a recent direction suggests using the topic model notion as a feature reduction method, in which the text is represented as a mixture of hidden topics, where the extracted latent topics from text documents form the features ( Onan et al, 2016 ). In other words, the textual data is represented as a bag of topics ( Zhou et al, 2009 ; Yousef et al, 2020a ) rather than a bag of words. However, in the short-text corpus, an advanced approach must be developed ( Al Qundus et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring

Yousef

Voskergian

2022

Front. Genet.

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Medical document classification is one of the active research problems and the most challenging within the text classification domain. Medical datasets often contain massive feature sets where many features are considered irrelevant, redundant, and add noise, thus, reducing the classification performance. Therefore, to obtain a better accuracy of a classification model, it is crucial to choose a set of features (terms) that best discriminate between the classes of medical documents. This study proposes TextNetTopics, a novel approach that applies feature selection by considering Bag-of-topics (BOT) rather than the traditional approach, Bag-of-words (BOW). Thus our approach performs topic selections rather than words selection. TextNetTopics is based on the generic approach entitled G-S-M (Grouping, Scoring, and Modeling), developed by Yousef and his colleagues and used mainly in biological data. The proposed approach suggests scoring topics to select the top topics for training the classifier. This study applied TextNetTopics to textual data to respond to the CAMDA challenge. TextNetTopics outperforms various feature selection approaches while highly performing when applying the model to the validation data provided by the CAMDA. Additionally, we have applied our algorithm to different textual datasets.

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“…Topic models, such as PLSA (Hofmann, 1999) and LDA (Blei et al, 2003), have shown great success in discovering latent topics in text collections. They have considerable applications in the information retrieval, text clustering and categorization (Zhou et al, 2009), word sense disambiguation (Boyd-Graber et al, 2007), etc.…”

Section: Introductionmentioning

confidence: 99%

Accounting ngrams and multi-word terms can improve topic models

Nokel

Loukachevitch

2016

Proceedings of the 12th Workshop on Multiword Expressions

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The paper presents an empirical study of integrating ngrams and multi-word terms into topic models, while maintaining similarities between them and words based on their component structure. First, we adapt the PLSA-SIM algorithm to the more widespread LDA model and ngrams. Then we propose a novel algorithm LDA-ITER that allows the incorporation of the most suitable ngrams into topic models. The experiments of integrating ngrams and multiword terms conducted on five text collections in different languages and domains demonstrate a significant improvement in all the metrics under consideration.

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Text Categorization Based on Topic Model

Cited by 23 publications

References 5 publications

Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets

Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets

TextNetTopics: Text Classification Based Word Grouping as Topics and Topics’ Scoring

Accounting ngrams and multi-word terms can improve topic models

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