Oriental Journal of Computer Science and Technology

Open Access Open Access  Restricted Access Subscription Access

An Automated Complex Word Identification from Text: A Survey


Affiliations
1 Department of Computer Science, Guru Nanak Dev University Amritsar. Punjab,, India
2 Dean Faculty of Engineering, Department of Computer Science, Guru Nanak Dev University Amritsar. Punjab, India
3 Department of Computer Science, Guru Nanak Dev University Amritsar. Punjab, India
 

Complex Word Identification (CWI) is the process of locating difficult words from a given sentence. The aim of automated CWI system is to make non-native English user understand the meaning of target word in the sentence. CWI systems assist second language learners and dyslexic users through simplification of text. This study introduces the CWI process and investigates the performance of twenty systems submitted in the SemEval -2016 for CWI. The G-score measure which is harmonic mean of accuracy and recall is taken for the performance evaluation of systems. This paper explores twenty CWI systems and identifies that why sv000gg system outperformed with highest G-score as 0.773 and 0.774 for the two respective submissions.

Keywords

CWI, Lexical Simplification, Textual Entailment, Text Classification.
User
Notifications
Font Size

  • Chandrasekar, R., Doran, C., & Srinivas, B. . Motivations and methods for text simplification. Proceedings of the 16th conference on Computational linguistics -.doi:10.3115/993268.993361, (1996).

  • Siddharthan, A. Syntactic Simplification and Text Cohesion. Research on Language and Computation, vol.4(1), pp.77-109. doi:10.1007/s11168-006-9011-1 (2006).

  • Specia, L. Translating from Complex to Simplified Sentences. Lecture Notes in Computer Science, doi:10.1007/978-3-64212320-7_5, pp.30-39. (2010).

  • De Belder, J., & Moens, M. . A Dataset for the Evaluation of Lexical Simplification. Computational Linguistics and Intelligent Text Processing,doi:10.1007/978-3-64228601-8_36, pp.426-437 (2012)

  • Di Marco, A., & Navigli, R. Clustering and Diversifying Web Search Results with GraphBased Word Sense Induction. Computational Linguistics, doi:10.1162/coli_a_00148, vol.39(3), pp.709-754. (2013).

  • Klapaftis, I. P., & Manandhar, S. Evaluating Word Sense Induction and Disambiguation Methods. Language Resources and Evaluation, doi:10.1007/s10579-012-9205-0 vol.47(3), pp.579-605. (2013).

  • Marelli, M., Bentivogli, L., Baroni, M., Bernardi, R., Menini, S., & Zamparelli, R. SemEval -2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). doi:10.3115/v1/ s14-2001 (2014).

  • Oepen, S., Kuhlmann, M., Miyao, Y., Zeman, D., Flickinger, D., Hajic, J., Zhang, Y. SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). doi:10.3115/v1/ s14-2008 (2014).

  • Agirre, E., Banea, C., Cardie, C., Cer, D., Diab, M., Gonzalez-Agirre, A., Wiebe, J. SemEval-2014 Task 10: Multilingual Semantic Textual Similarity. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). doi:10.3115/v1/ s14-2010 (2014).

  • Moro, A., & Navigli, R. SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). doi:10.18653/ v1/s15-2049 (2015).

  • Paetzold, G., & Specia, L. (2015). LEXenstein: A Framework for Lexical Simplification.Proceedings of ACL-IJCNLP 2015 System Demonstrations. doi:10.3115/v1/p15-4015

  • Paetzold, G., & Specia, L. SemEval 2016 Task 11: Complex Word Identification. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval2016). doi:10.18653/v1/s16-1085, (2016).

  • Davoodi, E., & Kosseim,L. CLaC at SemEval2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). doi:10.18653/ v1/s16-1151 (2016).

  • Konkol, M. (2016). UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval- 2016). doi:10.18653/ v1/s16-1162

  • Kuru, O. (2016). AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval2016). doi:10.18653/v1/s16-1163

  • Martínez Martínez, J. M., & Tan, L. USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). doi:10.18653/ v1/s16-1147, (2016).

  • Paetzold, G., & Specia, L. SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval -2016). doi:10.18653/v1/s16-1149 (2016).

  • Sp, S., Kumar,A., & K P, S. AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval2016). doi:10.18653/v1/s16-1159 (2016).

  • Wróbel, K. PLUJAGH at SemEval-2016 Task 11: Simple System for Complex Word Identification. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval- 2016). doi:10.18653/ v1/s16-1146, (2016).

  • Choubey, P., & Pateria, S. Garuda and Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning Models. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). doi:10.18653/ v1/s16-1156, (2016).

  • Malmasi, S., Dras, M., & Zampieri, M. LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval2016). doi:10.18653/v1/s16-1154, (2016).


Abstract Views: 292

PDF Views: 0




  • An Automated Complex Word Identification from Text: A Survey

Abstract Views: 292  |  PDF Views: 0

Authors

Jaspreet Singh
Department of Computer Science, Guru Nanak Dev University Amritsar. Punjab,, India
Gurvinder Singh
Dean Faculty of Engineering, Department of Computer Science, Guru Nanak Dev University Amritsar. Punjab, India
Rajinder Singh Virk
Department of Computer Science, Guru Nanak Dev University Amritsar. Punjab, India

Abstract


Complex Word Identification (CWI) is the process of locating difficult words from a given sentence. The aim of automated CWI system is to make non-native English user understand the meaning of target word in the sentence. CWI systems assist second language learners and dyslexic users through simplification of text. This study introduces the CWI process and investigates the performance of twenty systems submitted in the SemEval -2016 for CWI. The G-score measure which is harmonic mean of accuracy and recall is taken for the performance evaluation of systems. This paper explores twenty CWI systems and identifies that why sv000gg system outperformed with highest G-score as 0.773 and 0.774 for the two respective submissions.

Keywords


CWI, Lexical Simplification, Textual Entailment, Text Classification.

References





DOI: https://doi.org/10.13005/ojcst%2F10.03.09