A Clustering Algorithm for Short Documents Based On Concept Similarity

Peng, Jing; Yang, Dongqing; Wang, Jianwei; Wu, Meng-qing; Wang, Jungang

doi:10.1109/pacrim.2007.4313172

Cited by 5 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…News, document titles, abstracts, FAQs, chats, etc., are some examples of the high volume of short texts available on the Internet. Therefore, there exists sufficient interest from the computational community to analyse the behaviour of categorization methods when using short text corpora [12][13][14][15][16][17][18]. If these short texts belong to the same domain (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Self-enriching Methodology for Clustering Narrow Domain Short Texts

Pinto

Rosso

Jiménez-Salazar

2010

The Computer Journal

View full text Add to dashboard Cite

Clustering narrow domain short texts is considered to be a complex task because of the intrinsic features of the corpus to be clustered: (i) the low frequencies of vocabulary terms in short texts, and (ii) the high vocabulary overlapping associated to narrow domains. The aim of this paper is to introduce a selfterm expansion methodology for improving the performance of clustering methods when dealing with corpora of this kind. This methodology allows raw textual data to be enriched by adding co-related terms from an automatically constructed lexical knowledge resource obtained from the same target data set (and not from an external resource). We also propose a set of supervised and unsupervised text assessment measures for evaluating different corpus features, such as shortness, stylometry and domain broadness. With the help of these measures, we may determine beforehand whether or not to use the methodology proposed in this paper. Finally, we integrate all these assessment measures in a freely available web-based system named Watermarking Corpora On-line System, which may be used by computer scientists in order to evaluate the different features associated with a given textual corpus.

show abstract

Section: Introductionmentioning

confidence: 99%

A Self-enriching Methodology for Clustering Narrow Domain Short Texts

Pinto

Rosso

Jiménez-Salazar

2010

The Computer Journal

View full text Add to dashboard Cite

show abstract

“…There are proposals of using dictionaries such as [24] and [46] widely used in word sense disambiguation. In [21] and [28] different ways of improving text clustering by employing ontologies, authors have reported the improvement of the similarity intra-documents by incorporating background knowledge from external resources such as WordNet.…”

mentioning

confidence: 99%

Prototype/topic based clustering method for weblogs

Perez-Tellez

Cardiff

Rosso

et al. 2016

IDA

View full text Add to dashboard Cite

Abstract. In the last 10 years, the information generated on weblog sites has increased exponentially, resulting in a clear need for intelligent approaches to analyse and organise this massive amount of information. In this work, we present a methodology to cluster weblog posts according to the topics discussed therein, which we derive by text analysis. We have called the methodology Prototype/Topic Based Clustering, an approach which is based on a generative probabilistic model in conjunction with a Self-Term Expansion methodology. The usage of the Self-Term Expansion methodology is to improve the representation of the data and the generative probabilistic model is employed to identify relevant topics discussed in the weblogs. We have modified the generative probabilistic model in order to exploit predefined initialisations of the model and have performed our experiments in narrow and wide domain subsets. The results of our approach have demonstrated a considerable improvement over the pre-defined baseline and alternative state of the art approaches, achieving an improvement of up to 20% in many cases. The experiments were performed on both narrow and wide domain datasets, with the latter showing better improvement. However in both cases, our results outperformed the baseline and state of the art algorithms.

show abstract

“…In [52,53,26,97] authors suggested different ways of improving text clustering results by using ontologies. They have obtained a better similarity intra-documents incorporating background knowledge (using the WordNet ontology [53,26] and the HowNet ontology [97], as mentioned in Chapter 3) into the document representation.…”

Section: Term Expansion Using External Knowledgementioning

confidence: 99%

“…They have obtained a better similarity intra-documents incorporating background knowledge (using the WordNet ontology [53,26] and the HowNet ontology [97], as mentioned in Chapter 3) into the document representation. In these papers it has been claimed that this procedure "always" improves performance compared to the best baseline.…”

Section: Term Expansion Using External Knowledgementioning

confidence: 99%

“…News, document titles, abstracts, FAQs, chats, etc., are some examples of the high volume of short texts available on Internet. Therefore, there exists sufficient interest from the computational linguistic community to analyse the behaviour of classifiers when using short-text corpora [153,54,154,111,21,11,97]. If the short texts belong to the same domain (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Clustering and Evaluation of Narrow Domain Short-Test Corpora

Avendaño¹,

Eduardo²

View full text Add to dashboard Cite

A Clustering Algorithm for Short Documents Based On Concept Similarity

Cited by 5 publications

References 6 publications

A Self-enriching Methodology for Clustering Narrow Domain Short Texts

A Self-enriching Methodology for Clustering Narrow Domain Short Texts

Prototype/topic based clustering method for weblogs

On Clustering and Evaluation of Narrow Domain Short-Test Corpora

Contact Info

Product

Resources

About