Cross-Lingual Word Sense Disambiguation for Languages with Scarce Resources

Sarrafzadeh, Bahareh; Yakovets, Nikolay; Cercone, Nick; An, Aijun

doi:10.1007/978-3-642-21043-3_42

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Table 7 also implies that querying FNLP is less time consuming than FarsNet, because it returns fewer and more relevant results. POSe and cross-lingual algorithm (Sarrafzadeh, Yakovets, Cercone, & An, 2011) are also benefited from lexical resources. POSe is almost faster than both HMM and ME plus FNLP but it is employing a smaller dataset and it is less precise.…”

Section: The Resultsmentioning

confidence: 99%

FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

2018

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Natural language processing is a composition of several error‐prone and challenging tasks, including part of speech tagging, word sense disambiguation, named entity recognition, and compound verb detection. Studying intrasentence relations and roles is essential to improve the mentioned subtasks. Semi‐automatic schemes such as ontologies can be applied to clarify word's dependencies. This paper presents an ontology that is targeting to improve POS tagging, WSD, NER, and compound verb detection in Persian with extra properties that may ameliorate machine translation. The ontology is tested in combinations with several state‐of‐art algorithms on Dadegan corpus. The results show that coping semantic analysis with machine learning methods enhance relation detection and consequently precision of the mentioned subtasks, which is not widely addressed in Persian. Furthermore, the experimental results declare that the accuracy rate increases between 4.5 and 23% for different tasks.

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Section: The Resultsmentioning

confidence: 99%

FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

2018

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“…Moreover, this research targets analysis of Persian user-generated textual content and the challenges that come with it, regrettably, there is an inadequate number of researches in exploiting machine learning approaches for the Persian language. On the other hand, analyzing Persian textual content also suffers from a lack of resources [6], [7] such as datasets and text manipulation tools.…”

Section: Introductionmentioning

confidence: 99%

The Challenges of Persian User-generated Textual Content: A Machine Learning-Based Approach

Habib

2021

Preprint

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Over recent years a lot of research papers and studies have been published on the development of effective approaches that benefit from a large amount of user-generated content and build intelligent predictive models on top of them. This research applies machine learning-based approaches to tackle the hurdles that come with Persian usergenerated textual content. Unfortunately, there is still inadequate research in exploiting machine learning approaches to classify/cluster Persian text. Further, analyzing Persian text suffers from a lack of resources; specifically from datasets and text manipulation tools. Since the syntax and semantics of the Persian language is different from English and other languages, the available resources from these languages are not instantly usable for Persian. In addition, recognition of nouns and pronouns, parts of speech tagging, finding words' boundary, stemming or character manipulations for Persian language are still unsolved issues that require further studying. Therefore, efforts have been made in this research to address some of the challenges. This presented approach uses a machine-translated datasets to conduct sentiment analysis for the Persian language. Finally, the dataset has been rehearsed with different classifiers and feature engineering approaches. The results of the experiments have shown promising state-of-the-art performance in contrast to the previous efforts; the best classifier was Support Vector Machines which achieved a precision of 91.22%, recall of 91.71%, and F1 score of 91.46%.

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“…In terms of related work addressing the CL-WSD problem in Persian, Sarrafzadeh et al [15] follows a knowledge-based approach by exploiting FarsNet [17]. However, since their evaluation collection is not available, the results are impossible to compare with other possible approaches.…”

Section: Introductionmentioning

confidence: 99%

Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian

Rekabsaz,

Lupu,

Hanbury

et al. 2017

Preprint

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We explore the use of unsupervised methods in Cross-Lingual Word Sense Disambiguation (CL-WSD) with the application of English to Persian. Our proposed approach targets the languages with scarce resources (low-density) by exploiting word embedding and semantic similarity of the words in context. We evaluate the approach on a recent evaluation benchmark and compare it with the state-of-the-art unsupervised system (CO-Graph). The results show that our approach outperforms both the standard baseline and the CO-Graph system in both of the task evaluation metrics (Out-Of-Five and Best result).

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Cross-Lingual Word Sense Disambiguation for Languages with Scarce Resources

Cited by 7 publications

References 12 publications

FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

FNLP‐ONT: A feasible ontology for improving NLP tasks in Persian

The Challenges of Persian User-generated Textual Content: A Machine Learning-Based Approach

Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian

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