Multilingual news clustering: Feature translation vs. identification of cognate named entities

Montalvo, Soto; Martínez, Raquel; Casillas, Arantza; Fresno, Víctor

doi:10.1016/j.patrec.2007.07.011

Cited by 14 publications

(12 citation statements)

References 15 publications

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“…Their experiments show that clustering based only on NEs is complementary to clustering based on keywords, because using more information about NEs in the clustering, as their category, produce meaningful layers and group of documents. In a previous work (Montalvo, Martínez, Casillas, & Fresno, ), we showed that the use of NEs as the only type of features to represent news documents leads to good multilingual news clustering results through the identification of equivalent NEs, either by translation or by cognate identification. Additionally, in Montalvo, Fresno, and Martínez () we proposed a new similarity measure based only on the NEs shared by news documents; the results were promising and comparable with those obtained with such well‐known similarity measures as the cosine or correlation coefficient.…”

Section: Related Workmentioning

confidence: 76%

“…Therefore, it is crucial to be able to establish as many correspondences as possible among common NEs, as much within the same language as between both sides of the comparable collections of news. In addition, NE categories are very useful as additional information for use in similarity evaluations of news documents because we can vary the importance of common entities depending on their category or we can combine the information of common entities differently depending on their category (Montalvo et al., , ).…”

Section: Representation Of the Collection Of Comparable Newsmentioning

confidence: 99%

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Exploiting named entities for bilingual news clustering

Montalvo

Martínez

Fresno

et al. 2014

Asso for Info Science & Tech

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In this article, we present a new algorithm for clustering a bilingual collection of comparable news items in groups of specific topics. Our hypothesis is that named entities (NEs) are more informative than other features in the news when clustering fine grained topics. The algorithm does not need as input any information related to the number of clusters, and carries out the clustering only based on information regarding the shared named entities of the news items. This proposal is evaluated using different data sets and outperforms other state-ofthe-art algorithms, thereby proving the plausibility of the approach. In addition, because the applicability of our approach depends on the possibility of identifying equivalent named entities among the news, we propose a heuristic system to identify equivalent named entities in the same and different languages, thereby obtaining good performance.

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Section: Related Workmentioning

confidence: 76%

Section: Representation Of the Collection Of Comparable Newsmentioning

confidence: 99%

Exploiting named entities for bilingual news clustering

Montalvo

Martínez

Fresno

et al. 2014

Asso for Info Science & Tech

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show abstract

“…Our approach for multilingual news clustering is based on the representation of the news documents by means of the cognate Named Entities they contain. Previously, we obtained encouraging preliminary results using only the cognate Named Entities as news representative features [43]. We measured the similarity between news documents by computing the Named Entities they shared.…”

Section: Multilingual News Clusteringmentioning

confidence: 99%

Quality prediction of multilingual news clustering: An experimental study

Montalvo

Martínez

Fresno

2015

Journal of Information Science

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The evaluation of clustering results is one of the most important issues in cluster analysis, a core task for effective information access. There are two types of measures for evaluating the quality of clustering results: internal and external. External validity measures evaluate how well the clustering results match prior knowledge about the data, whereas internal measures do not need external information, dealing only with information within the data. In this regard, the main drawback of external evaluation measures is that they are not applicable in real-world situations. In this paper we present an experimental study to determine whether it is possible to predict the quality of multilingual news clustering results by means of an internal evaluation measure. We study whether the internal evaluation measure Expected Density correlates with the external measure F-measure, the most common way of evaluating clustering results. In the experiments, we use different data collections, clustering algorithms and similarity measures in order to determine their influence in the correlation between those measures. Regarding similarity measures, another important issue in clustering, we propose a new similarity measure to calculate how similar two news documents are. This measure is based on the Named Entities shared by both documents. The results show that correlation depends on several different factors, such as the type of collection, the granularity of the clusters, the type of algorithm and the similarity measure.

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“…To the best of our knowledge, existing clustering approaches for comparable corpora are customized for a small set (two or three) of languages (Montalvo et al, 2007). Most of them are not generalizable to many languages as they employ bilingual dictionaries and the translation is performed sequentially considering only pairs of languages.…”

Section: Introductionmentioning

confidence: 99%

Semantic-Based Multilingual Document Clustering via Tensor Modeling

Romeo¹,

Tagarelli²,

Ienco³

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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A major challenge in document clustering research arises from the growing amount of text data written in different languages. Previous approaches depend on language-specific solutions (e.g., bilingual dictionaries, sequential machine translation) to evaluate document similarities, and the required transformations may alter the original document semantics. To cope with this issue we propose a new document clustering approach for multilingual corpora that (i) exploits a large-scale multilingual knowledge base, (ii) takes advantage of the multi-topic nature of the text documents, and (iii) employs a tensor-based model to deal with high dimensionality and sparseness. Results have shown the significance of our approach and its better performance w.r.t. classic document clustering approaches, in both a balanced and an unbalanced corpus evaluation.

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Multilingual news clustering: Feature translation vs. identification of cognate named entities

Cited by 14 publications

References 15 publications

Exploiting named entities for bilingual news clustering

Exploiting named entities for bilingual news clustering

Quality prediction of multilingual news clustering: An experimental study

Semantic-Based Multilingual Document Clustering via Tensor Modeling

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