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CITATION |
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NAKAJIMA, Y., PTASZYNSKI, M., HONMA, H., & MASUI, F. (2016). An Extraction Method for Future Reference Expressions Using Morphological and Semantic Patterns. |
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Dzulkifli, M. A., bin Abdul Rahman, A. W., Badi, J. A. B., & Solihu, A. K. H. (2016). Routes to Remembering: Lessons from al Huffaz. Mediterranean Journal of Social Sciences, 7(3 S1), 121. |
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3 |
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Haroon, R. P., & Shaharban, T. A. Malayalam machine translation using hybrid approach. |
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4 |
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Badaro, G., Baly, R., Akel, R., Fayad, L., Khairallah, J., Hajj, H., ... & Shaban, K. B. (2015, July). A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets. In ANLP Workshop 2015 (p. 18). |
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5 |
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Negesse, F. (2015). Classification of Oromo Dialects: A Computational Approach. |
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6 |
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Siddiqui, M. A., Dahab, M. Y., & Batarfi, O. A. (2015). Building A Sentiment Analysis Corpus With Multifaceted Hierarchical Annotation. |
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7 |
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Duwairi, R. M., Ahmed, N. A., & Al-Rifai, S. Y. Detecting sentiment embedded in Arabic social media–A lexicon-based approach. Journal of Intelligent and Fuzzy Systems. |
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8 |
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Ali, M. N. Y., Sorwar, G., Toru, A., Islam, M. A., & Shamsujjoha, M. (2015). Morphological Rules of Bangla Repetitive Words for UNL Based Machine Translation. In Advances in Swarm and Computational Intelligence (pp. 401-408). Springer International Publishing. |
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9 |
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Siddiqui, M. A., Dahab, M. Y., & Batarfi, O. A. (2015). Building A Sentiment Analysis Corpus With Multifaceted Hierarchical Annotation. |
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10 |
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Nijim, A., El Shenawy, A., Mostafa, M. T., & Alez, R. A. Anovel Approach for recognizing text in arabic ancient manuscripts. |
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11 |
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Afzal, N., & Bawakid, A. (2015). Comparison between Surface-based and Dependency-based Relation Extraction Approaches for Automatic Generation of Multiple-Choice Questions. |
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12 |
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Wegari, G. M., Melucci, M., & Teferra, S. (2015, September). Suffix sequences based morphological segmentation for Afaan Oromo. In AFRICON, 2015 (pp. 1-6). IEEE. |
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13 |
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Al-Bakry, A. M., & Al-Rikaby, M. K. Enhanced Levenshtein Edit Distance Method functioning as a String-to-String Similarity Measure. Distributed Agents for Web Content Filtering, 48. |
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14 |
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Saha, A. K., Mridha, M., Hussein, M. R., & Das, J. K. (2015, June). Design and implementation of an efficient DeConverter for generating Bangla sentences from UNL expression. In Informatics, Electronics & Vision (ICIEV), 2015 International Conference on (pp. 1-6). IEEE. |
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15 |
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Alcalde, J. (2015). Linguistic justice: an interdisciplinary overview of the literature. |
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16 |
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Alotaibi, S. S. (2015). Sentiment Analysis in the Arabic Language Using Machine Learning (Doctoral dissertation, Colorado State University. Libraries). |
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17 |
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Eskander, R., & Rambow, O. SLSA: A Sentiment Lexicon for Standard Arabic. |
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18 |
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Godase, A., & Govilkar, S. machine translation development for indian languages and its approaches. |
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19 |
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Seedah, D. P. K. (2014). Retrieving information from heterogeneous freight data sources to answer natural language queries (Doctoral dissertation). |
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20 |
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Abdalkader, M. (2014). Sentiment Analysis of Egyptian Arabic in Social Media. |
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21 |
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Shirvi, N. N., & Panchal, M. H. (2014). Translation of English Algorithm in C Program using Syntax Directed Translation Schema. |
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22 |
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NAKAJIMA, Y., PTASZYNSKI, M., HONMA, H., & MASUI, F. (2014). FAN-14-029 Extraction of Future Reference Expressions in Trend Information. ? nn te ri ji e nn Suites ? su Te Rousseau · ? nn Polyster ji ? Rousseau Lecture Proceedings, 2014 (24) , 129-134. |
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23 |
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Larcom, M. K. (2014). The Minimalist Machine: An Implementation of Arabic Structures and Syntax. |
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24 |
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Badaro, G., Baly, R., Hajj, H., Habash, N., & El-Hajj, W. (2014). A large scale Arabic sentiment lexicon for Arabic opinion mining. ANLP 2014, 165. |
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25 |
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Bayoudhi, A., Koubaa, H., Belguith, L. H., & Ghorbel, H. Vers un lexique arabe pour l’analyse des opinions et des sentiments. |
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26 |
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Alrabiah, M., Al-Salman, A., & Atwell, E. (2014, October). The refined MI: A significant improvement to mutual information. In Asian Language Processing (IALP), 2014 International Conference on (pp. 132-135). IEEE. |
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27 |
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Alrabiah, M., Al-salman, A., & Atwell, E. A New Distributional Semantic Model for Classical Arabic. |
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28 |
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Atwell, E., & Alfaifi, A. Arabic corpus linguistics research at the University of Leeds. |
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29 |
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Atwell, E., & Alfaifi, A. Arabic corpus linguistics research at the University of Leeds. |
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30 |
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D'hondt, E. K. L. (2014). Cracking the patent: using phrasal representations to aid patent classfication. [Sl: sn]. |
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31 |
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Bijimol, T. K., & Abraham, J. T. (2014). A Study of Machine Translation Methods. |
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32 |
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Ptaszynski, M., Masui, F., Rzepka, R., & Araki, K. (2014). First Glance on Pattern-based Language Modeling. Language Acquisition and Understanding Research Group (LAU), Technical Reports, Summer. |
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33 |
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Nakajima, Y., Ptaszynski, M., Honma, H., & Masui, F. (2014, March). Investigation of Future Reference Expressions in Trend Information. In Proceedings of the 2014 AAAI Spring Symposium Series (pp. 31-38). |
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34 |
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Ptaszynski, M., Masui, F., Rzepka, R., & Araki, K. (2014). Detecting emotive sentences with pattern-based language modelling. Procedia Computer Science, 35, 484-493. |
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35 |
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Ptaszynski, M., Masui, F., Rzepka, R., & Araki, K. (2014). Automatic Extraction of Emotive and Non-emotive Sentence Patterns. In Proceedings of The Twentieth Annual Meeting of The Association for Natural Language Processing (NLP2014) (pp. 868-871). |
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36 |
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Abbas, Q. (2014). A Stochastic Prediction Interface for Urdu. International Journal of Intelligent Systems and Applications (IJISA), 7(1), 94. |
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37 |
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Sakuta, H., & Adachi, E. How Differently Do We Talk? A Study of Sentence Patterns in Groups of Different Age, Gender and Social Status. |
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38 |
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Abbas, Q. (2014). Building Computational Resources: The URDU. KON-TB Treebank and the Urdu Parser (Doctoral dissertation). |
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39 |
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Abbas, Q. (2014, August). Semi-semantic part of speech annotation and evaluation. In Proceedings of ACL 8th Linguistic Annotation Workshop held in conjunction with COLING, Association of Computational Linguistics, P (pp. 75-81). |
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40 |
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Kulkarni, S., & Sagar, B. M. (2014). A Survey on Named Entity Recognition for South Indian Languages. |
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41 |
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Hinkova, A., Bubnik, Z., & Kadlec, P. (2014). Chemical Composition of Sugar and Confectionery Products. In Handbook of Food Chemistry (pp. 1-34). Springer Berlin Heidelberg. |
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42 |
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Girma, T., Landage, S. M., Wasif, A. I., Dhuppe, P., Kumar, M., & Sharma, S. (2014). Human language technologies and Affan Oromo. International Journal of Advanced Research in Engineering and Applied Sciences, 3(5), 1-13. |
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43 |
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Thapar, P. (2014). A Hybrid Approach used to Stem Punjabi Words. |
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44 |
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Seedah, D. P. K. (2014). Retrieving information from heterogeneous freight data sources to answer natural language queries (Doctoral dissertation). |
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45 |
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Misikir, T. (2013). Developing a Stemming Algorithm for Awngi Text (Doctoral dissertation, AAU). |
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46 |
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Christensen, H., Green, P. D., & Hain, T. (2013, August). Learning speaker-specific pronunciations of disordered speech. In Interspeech (pp. 1159-1163). |
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47 |
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De conception, d. d. i. etiquetage morphosyntaxique du yoruba standard, une langue de la famille niger-congo et perspectives pour les langues nationales du benin. |
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48 |
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Ptaszynski, M. Taking Affect Analysis One Step Higher: The Idea of Contextual Appropriateness of Emotions and Its Perspectives. |
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49 |
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Ptaszynski, M., Dybala, P., Mazur, M., Rzepka, R., Araki, K., & Momouchi, Y. (2013). Towards Computational Fronesis: Verifying Contextual Appropriateness of Emotions. International Journal of Distance Education Technologies (IJDET), 11(2), 16-47. |
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50 |
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Khanam, M. H. experiments in probabilistic context free grammar for urdu language. |
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51 |
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Sarker, M. Z. H., Ali, M. N. Y., & Das, J. K. Generation Rules to Deconvert UNL Expressions to Bangla Sentences. |
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52 |
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Tamburini, F. (2013). The AnIta-Lemmatiser: A Tool for Accurate Lemmatisation of Italian Texts. In Evaluation of Natural Language and Speech Tools for Italian (pp. 266-273). Springer Berlin Heidelberg. |
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53 |
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Nigussie, E. (2013). Afaan Oromo–Amharic Cross Lingual Information Retrieval (Doctoral dissertation, AAU). |
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54 |
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Nakajima, Y., Ptaszynski, M., Honma, H., & Masui, F. Extracting References to the Future from News using Morphosemantic Patterns. |
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55 |
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Agrawal, A. J., & Kakde, O. G. (2013). Semantic analysis of natural language queries using domain ontology for information access from database. International Journal of Intelligent Systems and Applications (IJISA), 5(12), 81. |
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56 |
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Ptaszynski, M., Dokoshi, H., Oyama, S., Rzepka, R., Kurihara, M., Araki, K., & Momouchi, Y. (2013). Affect analysis in context of characters in narratives. Expert Systems with Applications, 40(1), 168-176. |
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57 |
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Morwal, S., & Chopra, D. (2013). nerhmm: A Tool For Named Entity Recognition based on Hidden Markov Model. International Journal on Natural Language Computing (IJNLC), 2, 43-49. |
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58 |
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Chopra, D., & Morwal, S. (2013). Named Entity Recognition in English Using Hidden Markov Model. International Journal. |
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59 |
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Jimmy, L., & Kaur, D. (2013). Named entity recognition in Manipuri: a hybrid approach. In Language Processing and Knowledge in the Web (pp. 104-110). Springer Berlin Heidelberg. |
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60 |
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Adedjouma Sèmiyou, A., Aoga, J. O., & Igue, M. A. (2013). Part-of-speech tagging of yoruba standard, language of niger-congo family. Res. Journal of Computer & IT Sciences, 1(1), 2-5. |
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61 |
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Ptaszynski, M., Masui, F., Dybala, P., Rzepka, R., & Araki, K. Open Source Affect Analysis System with Extensions. |
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62 |
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Morwal, S., Chopra, D., & Purohit, G. N. named entity recognition in natural languages using transliteration. |
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63 |
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Prasad, T. V., & Muthukumaran, G. M. Telugu to English Translation using Direct Machine Translation Approach. |
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64 |
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Nagarsekar, U., Mhapsekar, A., Kulkarni, P., & Kalbande, D. R. (2013, December). Emotion detection from “the SMS of the internet”. In Intelligent Computational Systems (RAICS), 2013 IEEE Recent Advances in (pp. 316-321). IEEE. |
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65 |
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D’hondt, E., Verberne, S., Weber, N., Koster, C., & Boves, L. (2012). Using skipgrams and pos-based feature selection for patent classification. Computational Linguistics in the Netherlands Journal, 2, 52-70. |
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66 |
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Jahan, N., Morwal, S., & Chopra, D. (2012). Named entity recognition in indian languages using gazetteer method and hidden markov model: A hybrid approach. IJCSET, March. |
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67 |
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Chopra, D., Jahan, N., & Morwal, S. (2012). Hindi named entity recognition by aggregating rule based heuristics and hidden markov model. International Journal of Information Sciences and Techniques (IJIST) Vol, 2. |
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68 |
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Chopra, D., & Morwal, S. (2012). Named Entity Recognition in Punjabi Using Hidden Markov Model. International Journal of Computer Science & Engineering Technology (IJCSET), 3(12). |
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69 |
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ABEDO, M. K. (2012). school of graduates studies department of information science (Doctoral dissertation, Addis Ababa University). |
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70 |
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Ptaszynski, M., Masui, F., Kimura, Y., Rzepka, R., & Araki, K. Extracting Patterns of Harmful Expressions for Cyberbullying Detection. |
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71 |
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Lempa, P., Ptaszynski, M., & Masui, F. Cyberbullying Blocker Application for Android. |
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72 |
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Morwal, S., Jahan, N., & Chopra, D. (2012). Named entity recognition using hidden Markov model (HMM). International Journal on Natural Language Computing (IJNLC), 1(4). |
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73 |
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Jahangir, F., Anwar, W., Bajwa, U. I., & Wang, X. (2012, December). N-gram and gazetteer list based named entity recognition for urdu: A scarce resourced language. In Proceedings of the 10th Workshop on Asian Language Resources (pp. 95-104). |
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74 |
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Tamburini, F., & Melandri, M. (2012). AnIta: a powerful morphological analyser for Italian. In LREC (pp. 941-947). |
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75 |
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Abbas, Q. (2012). Building a hierarchical annotated corpus of urdu: the URDU. KON-TB treebank. In Computational Linguistics and Intelligent Text Processing (pp. 66-79). Springer Berlin Heidelberg. |
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76 |
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Ptaszynski, M., Hasegawa, D., & Masui, F. Women Like Backchannel, But Men Finish Earlier: Pattern Based Language Modeling of Conversations Reveals Gender and Social Distance Differences. |
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77 |
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Sathyanarayana, S. A. S. A Hybrid approach for Named Entity Recognition, Classification and Extraction (NERCE) in Kannada Documents. |
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78 |
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Althobaiti, M., Kruschwitz, U., & Poesio, M. (2012, September). Identifying named entities on a university intranet. In Computer Science and Electronic Engineering Conference (CEEC), 2012 4th (pp. 94-99). IEEE. |
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79 |
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Chopra, D., Morwal, S., & Purohit, G. N. hidden markov model based named entity recognition tool. |
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80 |
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Kumar, D., & Rana, P. (2011). Stemming of Punjabi Words by using Brute Force Technique. International Journal of Engineering Science and Technology (IJEST) Vol, 3, 1351-1357. |
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81 |
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Tesfaye, D. (2011). A rule-based Afan Oromo Grammar Checker. IJACSA Editorial. |
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82 |
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YONAS, F. (2011). Development of stemming algorithm for Tigrigna text. |
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83 |
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HENOK, B. (2011). Dsp based impelementation of field-weakening on synchronous motor for high speed operation (doctoral dissertation, aau). |
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84 |
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Tamburini, F. (2011). The anita-lemmatiser. Working Notes of EVALITA. |
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85 |
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Fisseha, Y. (2011). Development of Stemming Algorithm for Tigrgna Text (Doctoral dissertation, AAU). |
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86 |
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Kumar, D. stemming of punjabi words by using brute force technique Dinesh Kumar Assistant Prof. & Head Department of Information Technology daviet, Jalandhar Prince Rana. |
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87 |
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Harrathi, R., Ouni, C., & Farhat, M. Impact de l’intégration de l’analyse morphologique de la langue arabe dans un système de recherche d’information open source. |
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88 |
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Abbas, Q. (2014). Building Computational Resources: The URDU. KON-TB Treebank and the Urdu Parser (Doctoral dissertation). |
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89 |
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Abbas, Q. Morphologically rich Urdu grammar parsing using Earley algorithm. Natural Language Engineering, 1-36. |
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90 |
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Abbas, Q. Morphologically rich Urdu grammar parsing using Earley algorithm. Natural Language Engineering, 1-36. |
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91 |
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Abbas, Q. (2014). Building Computational Resources: The URDU. KON-TB Treebank and the Urdu Parser (Doctoral dissertation). |
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