Automatic Labeling of Topics

Magatti, D.; Calegari, Silvia; Ciucci, Davide; Stella, Fabio

doi:10.1109/isda.2009.165

Cited by 47 publications

(27 citation statements)

References 10 publications

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“…Topic labeling task can be laborious, specifically when number of topics is substantial. Automatic topic labeling which aims to to automatically generate interpretable labels for the topics has attracted increasing attention in recent years [25], [26], [27], [28], [29]. Unlike previous works that have essentially concentrated on the topics discovered from LDA topic model and represented the topics by words, we propose an Knowledge-based topic model, KB-LDA, where topics are labeled by ontological concepts.…”

Section: Motivating Examplementioning

confidence: 99%

A Knowledge-based Topic Modeling Approach for Automatic Topic Labeling

Allahyari¹,

Pouriyeh²,

Kochut³

et al. 2017

ijacsa

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Abstract-Probabilistic topic models, which aim to discover latent topics in text corpora define each document as a multinomial distributions over topics and each topic as a multinomial distributions over words. Although, humans can infer a proper label for each topic by looking at top representative words of the topic but, it is not applicable for machines. Automatic Topic Labeling techniques try to address the problem. The ultimate goal of topic labeling techniques are to assign interpretable labels for the learned topics. In this paper, we are taking concepts of ontology into consideration instead of words alone to improve the quality of generated labels for each topic. Our work is different in comparison with the previous efforts in this area, where topics are usually represented with a batch of selected words from topics. We have highlighted some aspects of our approach including: 1) we have incorporated ontology concepts with statistical topic modeling in a unified framework, where each topic is a multinomial probability distribution over the concepts and each concept is represented as a distribution over words; and 2) a topic labeling model according to the meaning of the concepts of the ontology included in the learned topics. The best topic labels are selected with respect to the semantic similarity of the concepts and their ontological categorizations. We demonstrate the effectiveness of considering ontological concepts as richer aspects between topics and words by comprehensive experiments on two different data sets. In another word, representing topics via ontological concepts shows an effective way for generating descriptive and representative labels for the discovered topics.

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Section: Motivating Examplementioning

confidence: 99%

A Knowledge-based Topic Modeling Approach for Automatic Topic Labeling

Allahyari¹,

Pouriyeh²,

Kochut³

et al. 2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…Mei et al (2007) label topics using statistically significant bigrams identified in a reference collection. Magatti et al (2009) introduced an approach for labelling topics that relied on two hierarchical knowledge resources labelled by humans, while Lau et al (2010) proposed selecting the most representative word from a topic as its label. Hulpus et al (2013) make use of structured data from DBpedia to label topics.…”

Section: Introductionmentioning

confidence: 99%

Labelling Topics using Unsupervised Graph-based Methods

Αλέτρας¹,

Stevenson²

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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This paper introduces an unsupervised graph-based method that selects textual labels for automatically generated topics. Our approach uses the topic keywords to query a search engine and generate a graph from the words contained in the results. PageRank is then used to weigh the words in the graph and score the candidate labels. The state-of-the-art method for this task is supervised (Lau et al., 2011). Evaluation on a standard data set shows that the performance of our approach is consistently superior to previously reported methods.

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“…More recent approaches have explored the use of external sources (e.g. Wikipedia, WordNet) for supporting the automatic labelling of topics by deriving candidate labels by means of lexical (Lau et al, 2011;Magatti et al, 2009;Mei et al, 2007) or graphbased (Hulpus et al, 2013) algorithms applied on these sources. Mei et al (2007) proposed an unsupervised probabilistic methodology to automatically assign a label to a topic model.…”

Section: Introductionmentioning

confidence: 99%

“…Methods relying on external sources for automatic labelling of topics include the work by Magatti et al (2009) which derived candidate topic labels for topics induced by LDA using the hierarchy obtained from the Google Directory service and expanded through the use of the OpenOffice English Thesaurus. Lau et al (2011) generated label candidates for a topic based on topranking topic terms and titles of Wikipedia articles.…”

Section: Introductionmentioning

confidence: 99%

Automatic Labelling of Topic Models Learned from Twitter by Summarisation

Basave

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic topic labelling approaches which depend on external knowledge sources become less applicable here since relevant articles/concepts of the extracted topics may not exist in external sources. In this paper we propose to address the problem of automatic labelling of latent topics learned from Twitter as a summarisation problem. We introduce a framework which apply summarisation algorithms to generate topic labels. These algorithms are independent of external sources and only rely on the identification of dominant terms in documents related to the latent topic. We compare the efficiency of existing state of the art summarisation algorithms. Our results suggest that summarisation algorithms generate better topic labels which capture event-related context compared to the top-n terms returned by LDA.

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Automatic Labeling of Topics

Cited by 47 publications

References 10 publications

A Knowledge-based Topic Modeling Approach for Automatic Topic Labeling

A Knowledge-based Topic Modeling Approach for Automatic Topic Labeling

Labelling Topics using Unsupervised Graph-based Methods

Automatic Labelling of Topic Models Learned from Twitter by Summarisation

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