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International Journal of Computational Linguistics (IJCL)
An International peer-review journal operated under CSC-OpenAccess Policy.
ISSN - 2180-1266
Published - Bi-Monthly   |   Established -    |   Year of Publication - 2018

SUBMISSION
April 30, 2019

NOTIFICATION
May 31, 2019

PUBLICATION
June 30, 2019

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CITATION ANALYSIS

IJCL Citation Impact
(91 - 0) / 39 = 2.333

Refer to Citation Report for 2018 for complete details.
 

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CITATION REPORT FOR IJCL

Below calculations are based on in-process citations that are extracted through Google Scholar.


Total Citations = 91
Self Citations = 0
Total Publications = 39


Citation Impact
(Total Citations - Self Citations) / Total Publications

Citation Impact
(91 - 0) / 39 = 2.333

 
SR
M-CODE
CITATION
1
NAKAJIMA, Y., PTASZYNSKI, M., HONMA, H., & MASUI, F. (2016). An Extraction Method for Future Reference Expressions Using Morphological and Semantic Patterns.
2
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.
3
Haroon, R. P., & Shaharban, T. A. Malayalam machine translation using hybrid approach.
4
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).
5
Negesse, F. (2015). Classification of Oromo Dialects: A Computational Approach.
6
Siddiqui, M. A., Dahab, M. Y., & Batarfi, O. A. (2015). Building A Sentiment Analysis Corpus With Multifaceted Hierarchical Annotation.
7
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.
8
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.
9
Siddiqui, M. A., Dahab, M. Y., & Batarfi, O. A. (2015). Building A Sentiment Analysis Corpus With Multifaceted Hierarchical Annotation.
10
Nijim, A., El Shenawy, A., Mostafa, M. T., & Alez, R. A. Anovel Approach for recognizing text in arabic ancient manuscripts.
11
Afzal, N., & Bawakid, A. (2015). Comparison between Surface-based and Dependency-based Relation Extraction Approaches for Automatic Generation of Multiple-Choice Questions.
12
Wegari, G. M., Melucci, M., & Teferra, S. (2015, September). Suffix sequences based morphological segmentation for Afaan Oromo. In AFRICON, 2015 (pp. 1-6). IEEE.
13
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.
14
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.
15
Alcalde, J. (2015). Linguistic justice: an interdisciplinary overview of the literature.
16
Alotaibi, S. S. (2015). Sentiment Analysis in the Arabic Language Using Machine Learning (Doctoral dissertation, Colorado State University. Libraries).
17
Eskander, R., & Rambow, O. SLSA: A Sentiment Lexicon for Standard Arabic.
18
Godase, A., & Govilkar, S. machine translation development for indian languages and its approaches.
19
Seedah, D. P. K. (2014). Retrieving information from heterogeneous freight data sources to answer natural language queries (Doctoral dissertation).
20
Abdalkader, M. (2014). Sentiment Analysis of Egyptian Arabic in Social Media.
21
Shirvi, N. N., & Panchal, M. H. (2014). Translation of English Algorithm in C Program using Syntax Directed Translation Schema.
22
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.
23
Larcom, M. K. (2014). The Minimalist Machine: An Implementation of Arabic Structures and Syntax.
24
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.
25
Bayoudhi, A., Koubaa, H., Belguith, L. H., & Ghorbel, H. Vers un lexique arabe pour l’analyse des opinions et des sentiments.
26
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.
27
Alrabiah, M., Al-salman, A., & Atwell, E. A New Distributional Semantic Model for Classical Arabic.
28
Atwell, E., & Alfaifi, A. Arabic corpus linguistics research at the University of Leeds.
29
Atwell, E., & Alfaifi, A. Arabic corpus linguistics research at the University of Leeds.
30
D'hondt, E. K. L. (2014). Cracking the patent: using phrasal representations to aid patent classfication. [Sl: sn].
31
Bijimol, T. K., & Abraham, J. T. (2014). A Study of Machine Translation Methods.
32
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.
33
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).
34
Ptaszynski, M., Masui, F., Rzepka, R., & Araki, K. (2014). Detecting emotive sentences with pattern-based language modelling. Procedia Computer Science, 35, 484-493.
35
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).
36
Abbas, Q. (2014). A Stochastic Prediction Interface for Urdu. International Journal of Intelligent Systems and Applications (IJISA), 7(1), 94.
37
Sakuta, H., & Adachi, E. How Differently Do We Talk? A Study of Sentence Patterns in Groups of Different Age, Gender and Social Status.
38
Abbas, Q. (2014). Building Computational Resources: The URDU. KON-TB Treebank and the Urdu Parser (Doctoral dissertation).
39
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).
40
Kulkarni, S., & Sagar, B. M. (2014). A Survey on Named Entity Recognition for South Indian Languages.
41
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.
42
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.
43
Thapar, P. (2014). A Hybrid Approach used to Stem Punjabi Words.
44
Seedah, D. P. K. (2014). Retrieving information from heterogeneous freight data sources to answer natural language queries (Doctoral dissertation).
45
Misikir, T. (2013). Developing a Stemming Algorithm for Awngi Text (Doctoral dissertation, AAU).
46
Christensen, H., Green, P. D., & Hain, T. (2013, August). Learning speaker-specific pronunciations of disordered speech. In Interspeech (pp. 1159-1163).
47
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.
48
Ptaszynski, M. Taking Affect Analysis One Step Higher: The Idea of Contextual Appropriateness of Emotions and Its Perspectives.
49
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.
50
Khanam, M. H. experiments in probabilistic context free grammar for urdu language.
51
Sarker, M. Z. H., Ali, M. N. Y., & Das, J. K. Generation Rules to Deconvert UNL Expressions to Bangla Sentences.
52
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.
53
Nigussie, E. (2013). Afaan Oromo–Amharic Cross Lingual Information Retrieval (Doctoral dissertation, AAU).
54
Nakajima, Y., Ptaszynski, M., Honma, H., & Masui, F. Extracting References to the Future from News using Morphosemantic Patterns.
55
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.
56
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.
57
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.
58
Chopra, D., & Morwal, S. (2013). Named Entity Recognition in English Using Hidden Markov Model. International Journal.
59
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.
60
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.
61
Ptaszynski, M., Masui, F., Dybala, P., Rzepka, R., & Araki, K. Open Source Affect Analysis System with Extensions.
62
Morwal, S., Chopra, D., & Purohit, G. N. named entity recognition in natural languages using transliteration.
63
Prasad, T. V., & Muthukumaran, G. M. Telugu to English Translation using Direct Machine Translation Approach.
64
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.
65
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.
66
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.
67
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.
68
Chopra, D., & Morwal, S. (2012). Named Entity Recognition in Punjabi Using Hidden Markov Model. International Journal of Computer Science & Engineering Technology (IJCSET), 3(12).
69
ABEDO, M. K. (2012). school of graduates studies department of information science (Doctoral dissertation, Addis Ababa University).
70
Ptaszynski, M., Masui, F., Kimura, Y., Rzepka, R., & Araki, K. Extracting Patterns of Harmful Expressions for Cyberbullying Detection.
71
Lempa, P., Ptaszynski, M., & Masui, F. Cyberbullying Blocker Application for Android.
72
Morwal, S., Jahan, N., & Chopra, D. (2012). Named entity recognition using hidden Markov model (HMM). International Journal on Natural Language Computing (IJNLC), 1(4).
73
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).
74
Tamburini, F., & Melandri, M. (2012). AnIta: a powerful morphological analyser for Italian. In LREC (pp. 941-947).
75
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.
76
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.
77
Sathyanarayana, S. A. S. A Hybrid approach for Named Entity Recognition, Classification and Extraction (NERCE) in Kannada Documents.
78
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.
79
Chopra, D., Morwal, S., & Purohit, G. N. hidden markov model based named entity recognition tool.
80
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.
81
Tesfaye, D. (2011). A rule-based Afan Oromo Grammar Checker. IJACSA Editorial.
82
YONAS, F. (2011). Development of stemming algorithm for Tigrigna text.
83
HENOK, B. (2011). Dsp based impelementation of field-weakening on synchronous motor for high speed operation (doctoral dissertation, aau).
84
Tamburini, F. (2011). The anita-lemmatiser. Working Notes of EVALITA.
85
Fisseha, Y. (2011). Development of Stemming Algorithm for Tigrgna Text (Doctoral dissertation, AAU).
86
Kumar, D. stemming of punjabi words by using brute force technique Dinesh Kumar Assistant Prof. & Head Department of Information Technology daviet, Jalandhar Prince Rana.
87
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.
88
Abbas, Q. (2014). Building Computational Resources: The URDU. KON-TB Treebank and the Urdu Parser (Doctoral dissertation).
89
Abbas, Q. Morphologically rich Urdu grammar parsing using Earley algorithm. Natural Language Engineering, 1-36.
90
Abbas, Q. Morphologically rich Urdu grammar parsing using Earley algorithm. Natural Language Engineering, 1-36.
91
Abbas, Q. (2014). Building Computational Resources: The URDU. KON-TB Treebank and the Urdu Parser (Doctoral dissertation).