Extracting Comparable Articles from Wikipedia and Measuring their Comparabilities

Saad, Motaz; Langlois, David; Smaïli, Kamel

doi:10.1016/j.sbspro.2013.10.620

Cited by 14 publications

(22 citation statements)

References 2 publications

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“…These methods are based on bilingual dictionaries [10,16,19], or based on cross-lingual Information retrieval (CL-IR) [7,1,21], or based on cross-lingual Latent Semantic Indexing (CL-LSI) system [2,11,6,14].…”

Section: Introductionmentioning

confidence: 99%

“…In the dictionary based method [10,16,19], two cross-lingual documents d a and d e are comparable if a maximum of words in d a are translations of words in d e , so a bilingual dictionary can be used to look-up the translation of words in both documents. The drawbacks of this approach are the dependency on bilingual dictionaries which are not always available, and the necessity to use morphological analyzers for languages that can be inflected.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, word-toword translations based on dictionaries can cause many errors. [19] proposed binary and cosine measures based on multi-WordNet [3] dictionary to compare Wikipedia and news articles. Both binary and cosine measures proposed by [19] require the source-target texts to be represented as vectors of aligned words.…”

Section: Introductionmentioning

confidence: 99%

“…[19] proposed binary and cosine measures based on multi-WordNet [3] dictionary to compare Wikipedia and news articles. Both binary and cosine measures proposed by [19] require the source-target texts to be represented as vectors of aligned words. Word weight for the binary measure is either 1 or 0 (presence or absence of the word), while it is the term frequency for the cosine measure.…”

Section: Introductionmentioning

confidence: 99%

“…The first one is monolingual: the English article is translated and then mapped into the LSI Arabic space; the second one is cross-lingual: Arabic and English articles are mapped into Arabic-English CL-LSI space. We also compare these methods to the dictionary based method proposed by [19] which is described above.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Lingual Semantic Similarity Measure for Comparable Articles

Saad¹,

Langlois²,

Smaïli³

2014

Advances in Natural Language Processing

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Abstract. We aim in this research to find and compare cross-lingual articles concerning a specific topic. So, we need a measure for that. This measure can be based on bilingual dictionaries or based on numerical methods such as Latent Semantic Indexing (LSI). In this paper, we use the LSI in two ways to retrieve Arabic-English comparable articles. The first one is monolingual: the English article is translated into Arabic and then mapped into the Arabic LSI space; the second one is crosslingual: Arabic and English documents are mapped into Arabic-English LSI space. Then, we compare LSI approaches to the dictionary-based approach on several English-Arabic parallel and comparable corpora. Results indicate that the performance of cross-lingual LSI approach is competitive to monolingual approach, or even better for some corpora. Moreover, both LSI approaches outperform the dictionary approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-Lingual Semantic Similarity Measure for Comparable Articles

Saad¹,

Langlois²,

Smaïli³

2014

Advances in Natural Language Processing

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A Fine-Grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

Abidi

Fohr

Jouvet

et al. 2019

Communications in Computer and Information Science

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The objective of this paper is to compare the opinions of two videos in two different languages. To do so, a fine-grained approach inspired from the appraisal theory is used to analyze the content of the videos that concern the same topic. In general, the methods devoted to sentiment analysis concern the study of the polarity of a text or an utterance. The appraisal approach goes further than the basic polarity sentiments and consider more detailed sentiments by covering additional attributes of opinions such as: Attitude, Graduation and Engagement. In order to achieve such a comparison, in AMIS (Chist-Era project), we collected a corpus of 1503 Arabic and 1874 English videos. These videos need to be aligned in order to compare their contents, that is why we propose several methods to make them comparable. Then the best one is selected to align them and to constitute the data-set necessary for the fine-grained sentiment analysis.

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