Cross-Lingual Text Categorization

Bel, Núria; Koster, C. H. A.; Villegas, Marta

doi:10.1007/978-3-540-45175-4_13

Cited by 103 publications

(62 citation statements)

References 15 publications

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“…Unfortunately, the above-mentioned problems regarding availability, accessibility, and performance still hold in this case. The effect of different translation strategies on CLTC has been investigated by Bel, Koster, and Villegas (2003), Rigutini, Maggini, and Liu (2005), and Wei, Lin, and Yang (2011).…”

Section: Exploiting External Multilingual Resourcesmentioning

confidence: 99%

Lightweight Random Indexing for Polylingual Text Classification

Moreo¹,

Esuli²,

Sebastiani

2016

jair

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Multilingual Text Classification (MLTC) is a text classification task in which documents are written each in one among a set L of natural languages, and in which all documents must be classified under the same classification scheme, irrespective of language. There are two main variants of MLTC, namely Cross-Lingual Text Classification (CLTC) and Polylingual Text Classification (PLTC). In PLTC, which is the focus of this paper, we assume (differently from CLTC) that for each language in L there is a representative set of training documents; PLTC consists of improving the accuracy of each of the |L| monolingual classifiers by also leveraging the training documents written in the other (|L| − 1) languages. The obvious solution, consisting of generating a single polylingual classifier from the juxtaposed monolingual vector spaces, is usually infeasible, since the dimensionality of the resulting vector space is roughly |L| times that of a monolingual one, and is thus often unmanageable. As a response, the use of machine translation tools or multilingual dictionaries has been proposed. However, these resources are not always available, or are not always free to use.One machine-translation-free and dictionary-free method that, to the best of our knowledge, has never been applied to PLTC before, is Random Indexing (RI). We analyse RI in terms of space and time efficiency, and propose a particular configuration of it (that we dub Lightweight Random Indexing -LRI). By running experiments on two well known public benchmarks, Reuters RCV1/RCV2 (a comparable corpus) and JRC-Acquis (a parallel one), we show LRI to outperform (both in terms of effectiveness and efficiency) a number of previously proposed machine-translation-free and dictionary-free PLTC methods that we use as baselines.

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Section: Exploiting External Multilingual Resourcesmentioning

confidence: 99%

Lightweight Random Indexing for Polylingual Text Classification

Moreo¹,

Esuli²,

Sebastiani

2016

jair

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“…But on the Wikipedia articles, JointLDA model achieves lower perplexity scores which indicate better predictability than a bag-of-word translation model. This leaves a possibility for JointLDA to be preferred over bag-of-word translation for applications like CLIR and Cross-lingual Text Categorization [2].…”

Section: Perplexity Of the Aligned Test Setmentioning

confidence: 99%

“…This situation raises the need for novel ways of organizing a multilingual corpus based on common topics/events, which could potentially be useful for many cross-lingual applications like Cross-Lingual Information Retrieval (CLIR) [1] and cross-lingual text classification [2]. Though there have been many attempts to mine the topical structure from a document corpus [3][4][5] most of these approaches operate in a monolingual scenario.…”

Section: Introductionmentioning

confidence: 99%

Extracting Multilingual Topics from Unaligned Comparable Corpora

Jagarlamudi

Daumé

2010

Lecture Notes in Computer Science

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Abstract. Topic models have been studied extensively in the context of monolingual corpora. Though there are some attempts to mine topical structure from cross-lingual corpora, they require clues about document alignments. In this paper we present a generative model called JointLDA which uses a bilingual dictionary to mine multilingual topics from an unaligned corpus. Experiments conducted on different data sets confirm our conjecture that jointly modeling the cross-lingual corpora offers several advantages compared to individual monolingual models. Since the JointLDA model merges related topics in different languages into a single multilingual topic: a) it can fit the data with relatively fewer topics. b) it has the ability to predict related words from a language different than that of the given document. In fact it has better predictive power compared to the bag-of-word based translation model leaving the possibility for JointLDA to be preferred over bag-of-word model for cross-lingual IR applications. We also found that the monolingual models learnt while optimizing the cross-lingual copora are more effective than the corresponding LDA models.

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“…Bel et al [9] were amongst the early pioneers examining crosslingual text categorization. They used the Rocchio algorithm, a popular learning method based on relevance feedback, and the Winnow algorithm, a method for learning a linear classifier from labeled examples, to categorize documents in multiple languages.…”

Section: A] Machine Translation Techniquesmentioning

confidence: 99%

A Survey of Multilingual Document Clustering

Moholkar¹

2017

ijecs

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The amount of multilingual documents generated on internet, is increasing day by day. Multilingual document clustering (MDC) is a technique of classifying documents in different languages. Classification of documents for the languages without labeled training data set is a major challenge. Two major approaches used till date are machine translation of documents for classification and use bilingual dictionaries for effective translation of trained classification models. This paper surveys various MDC challenge and techniques. The major focus is on the problem of translating documents and classifying it semantically.

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Cross-Lingual Text Categorization

Cited by 103 publications

References 15 publications

Lightweight Random Indexing for Polylingual Text Classification

Lightweight Random Indexing for Polylingual Text Classification

Extracting Multilingual Topics from Unaligned Comparable Corpora

A Survey of Multilingual Document Clustering

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