A Natural Language Model of Computing with Words in Web Pages

PACLIC 20 / Wuhan, China / 1-3 November, 200

Detection of traits in students with suicidal tendencies on Internet applying Web Mining

This article presents an Internet data analysis model based on Web Mining with the aim to find knowledge about large amounts of data in cyberspace. To test the proposed method, suicide web pages were analyzed as a study case to identify and detect traits in students with suicidal tendencies. The procedure considers a Web Scraper to locate and download information from the Internet, as well as Natural Language Processing techniques to retrieve the words. To explore the information, a dataset based on Dynamic Tables and Semantic Ontologies was constructed, specifying the predictive variables in young people with suicidal inclination. Finally, to evaluate the efficiency of the model, Machine Learning and Deep Learning algorithms were used. It should be noticed that the procedures for the construction of the dataset (using Genetic Algorithms) and obtaining the knowledge (using Parallel Computing and Acceleration with GPU) were optimized. The results reveal an accuracy of 96.28% on the detection of characteristics in adolescents with suicidal tendencies, reaching the best result through a Recurrent Neural Network with 98% accuracy. It is inferred that the model is viable to establish bases on mechanisms of action and prevention of suicidal behaviors, which can be implemented in educational institutions or different social actors

A hybrid approach for transliterated word-level language identification: CRF with post processing heuristics

© {Owner/Author | ACM} {Year}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation, http://dx.doi.org/10.1145/2824864.2824876[EN] In this paper, we describe a hybrid approach for word-level language (WLL) identification of Bangla words written in Roman script and mixed with English words as part of our participation in the shared task on transliterated search at Forum for Information Retrieval Evaluation (FIRE) in 2014. A CRF based machine learning model and post-processing heuristics are employed for the WLL identification task. In addition to language identification, two transliteration systems were built to transliterate detected Bangla words written in Roman script into native Bangla script. The system demonstrated an overall token level language identification accuracy of 0.905. The token level Bangla and English language identification F-scores are 0.899, 0.920 respectively. The two transliteration systems achieved accuracies of 0.062 and 0.037. The word-level language identification system presented in this paper resulted in the best scores across almost all metrics among all the participating systems for the Bangla-English language pair.We acknowledge the support of the Department of Electronics and Information Technology (DeitY), Government of India, through the project “CLIA System Phase II”. The research work of the last author was carried out in the framework of WIQ-EI IRSES (Grant No. 269180) within the FP 7 Marie Curie, DIANA-APPLICATIONS (TIN2012-38603-C02-01) projects and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Banerjee, S.; Kuila, A.; Roy, A.; Naskar, SK.; Rosso, P.; Bandyopadhyay, S. (2014). A hybrid approach for transliterated word-level language identification: CRF with post processing heuristics. En FIRE '14 Proceedings of the Forum for Information Retrieval Evaluation. ACM. 170-173. https://doi.org/10.1145/2824864.2824876S170173Y. Al-Onaizan and K. Knight. Named entity translation: Extended abstract. In HLT, pages 122--124. Singapore, 2002.P. J. Antony, V. P. Ajith, and K. P. Suman. Feature extraction based english to kannada transliteration. In In hird International conference on Semantic E-business and Enterprise Computing. SEEC 2010, 2010.P. J. Antony, V. P. Ajith, and K. P. Suman. Kernel method for english to kannada transliteration. In International conference on-Recent trends in Information, Telecommunication and computing. ITC2010, 2010.M. Arbabi, S. M. Fischthal, V. C. Cheng, and E. Bart. Algorithms for arabic name transliteration. In IBM Journal of Research and Development, page 183. TeX Users Group, 1994.S. Banerjee, S. Naskar, and S. Bandyopadhyay. Bengali named entity recognition using margin infused relaxed algorithm. In TSD, pages 125--132. Springer International Publishing, 2014.U. Barman, J. Wagner, G. Chrupala, and J. Foster. Identification of languages and encodings in a multilingual document. page 127. EMNLP, 2014.K. R. Beesley. Language identifier: A computer program for automatic natural-language identification of on-line text. pages 47--54. ATA, 1988.P. F. Brown, S. A. D. Pietra, V. J. D. Pietra, and R. L. Mercer. Mercer: The mathematics of statistical machine translation: parameter estimation. pages 263--311. Computational Linguistics, 1993.M. Carpuat. Mixed-language and code-switching in the canadian hansard. page 107. EMNLP, 2014.G. Chittaranjan, Y. Vyas, K. Bali, and M. Choudhury. Word-level language identification using crf: Code-switching shared task report of msr india system. pages 73--79. EMNLP, 2014.A. Das, A. Ekbal, T. Mandal, and S. Bandyopadhyay. English to hindi machine transliteration system at news. pages 80--83. Proceeding of the Named Entities Workshop ACL-IJCNLP, Singapore, 2009.A. Ekbal, S. Naskar, and S. Bandyopadhyay. A modified joint source channel model for transliteration. pages 191--198. COLING-ACL Australia, 2006.I. Goto, N. Kato, N. Uratani, and T. Ehara. Transliteration considering context information based on the maximum entropy method. pages 125--132. MT-Summit IX, New Orleans, USA, 2003.R. Haque, S. Dandapat, A. K. Srivastava, S. K. Naskar, and A. Way. English to hindi transliteration using context-informed pb-smt:the dcu system for news 2009. NEWS 2009, 2009.S. Y. Jung, S. Hong, and E. Paek. An english to korean transliteration model of extended markov window.S. Y. Jung, S. L. Hong, and E. Paek. An english to korean transliteration model of extended markov window. pages 383--389. COLING, 2000.B. J. Kang and K. S. Choi. Automatic transliteration and back-transliteration by decision tree learning. LERC, May 2000.B. King and S. Abney. Labeling the languages of words in mixed-language documents using weakly supervised methods. pages 1110--1119. NAACL-HLT, 2013.R. Kneser and H. Ney. Improved backing-off for m-gram language modeling. In ICASSP, pages 181--184. Detroit, MI, 1995.R. Kneser and H. Ney. SRILM-an extensible language modeling toolkit. In Intl. Conf. on Spoken Language Processing, pages 901--904, 2002.K. Knight and J. Graehl. Machine transliteration. in computational linguistics. pages 599--612, 1998.P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, and E. Herbst. Moses: open source toolkit for statistical machine translation. In ACL, pages 177--180, 2007.P. Koehn, F. J. Och, and D. Marcu. Statistical phrase-based translation. In HLT-NAACL, 2003.A. Kumaran and T. Kellner. A generic framework for machine transliteration. In 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 721--722. ACM, 2007.H. Li, Z. Min, and J. Su. A joint source-channel model for machine transliteration. In ACL, page 159, 2004.C. Lignos and M. Marcus. Toward web-scale analysis of codeswitching. In Annual Meeting of the Linguistic Society of America, 2013.J. H. Oh and K. S. Choi. An english-korean transliteration model using pronunciation and contextual rules. In 19th international conference on Computational linguistics. ACL, 2002.T. Rama and K. Gali. Modeling machine transliteration as a phrase based statistical machine translation problem. In Language Technologies Research Centre. IIIT, Hyderabad, India, 2009.A. K. Singh and J. Gorla. Identification of languages and encodings in a multilingual document. In ACL-SIGWAC's Web As Corpus3, page 95. Presses univ. de Louvain, 2007.V. Sowmya, M. Choudhury, K. Bali, T. Dasgupta, and A. Basu. Resource creation for training and testing of transliteration systems for indian languages. In LREC, pages 2902--2907, 2010.V. Sowmya and V. Varma. Transliteration based text input methods for telugu. In ICCPOL-2009, 2009.B. G. Stalls and J. Graehl. Translating names and technical terms in arabic text. In Workshop on Computational Approaches to Semitic Languages, pages 34--41. ACL, 1998.S. Sumaja, R. Loganathan, and K. P. Suman. English to malayalam transliteration using sequence labeling approach. International Journal of Recent Trends in Engineering, 1(2), 2009.M. S. Vijaya, V. P. Ajith, G. Shivapratap, and K. P. Soman. English to tamil transliteration using weka. International Journal of Recent Trends in Engineering, 2009

Fully Automated Fact Checking Using External Sources

Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.Comment: RANLP-201