Abstract
Collocation plays an important role in English article writing. This research builds a collocation corpus for academic writings in engineering and science fields. Based on the collocation corpus, this research also establishes a sentence-wide collocation recommendation and error detection system for academic writing. The corpus is built from Science Citation Index (SCI) papers and industry field thesis, which are collected and processed by a formal procedure developed in this research. The first step of the procedure uses the Stanford Parser to parse and retrieve collocations sentence by sentence from those papers and thesis. The second step classifies these collected collocations in different types and gathers their information to establish a collocation corpus specifically for academic article writings. The use of the corpus is through a web-based collocation system built in this study. Distinguished from other collocation systems found on the web nowadays, the system can do full sentence collocation error detections and recommendations. After several conducted experiments, the system is proved capable of giving satisfied feedbacks and recommendations for scientific article authors. Although the collocation corpus now is not complete enough to give the most precise results, the formal procedure can still keep enhancing the corpus and improving the system by automatically collecting articles from various fields.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lewis, M.: Implementing the Lexical Approach. Thomson Heinle, Boston (2002)
Oxford University Press: Oxford Collocations Dictionary for Students of English (2002)
Chen, Y.-C., Yen, T.-X., Chang, J.S.: Associating collocations with WordNet senses using hybrid models. In: Proceedings of the Twenty-Fourth Conference on Computational Linguistics and Speech Processing (2012)
Benson, M., Benson, E., Ilson, R.F.: The BBI Combinatory Dictionary of English: A Guide to Word Combinations (1986)
Smadja, F.: Lexical Co-occurrence: The Missing Link Journal for Literary and Linguistic Computing (1989)
Smadja, F.: Retrieving Collocations from Text: Xtract. Association for Computational Linguistics (1993)
Church, K.W., Hanks, P.: Word Association Norms, Mutual Information, And Lexicography (1990)
Aji, S., & Kaimal, R. (2012). DOCUMENT SUMMARIZATION USING POSITIVE POINTWISE MUTUAL INFORMATION. International Journal of Computer Science & Information Technology, 4
Bouma, G.: Normalized (Pointwise) mutual information in collocation extraction. In: Proceedings of the Biennial GSCL Conference (2009)
Clear, J.: T-score and mutual information score from Birmingham Corpus website. http://lingua.mtsu.edu/chinese-computing/docs/tscore.html
Thanopoulos, A., Fakotakis, N., Kokkinakis, G.: Comparative evaluation of collocation extraction metrics. In: The International Conference on Language Resources and Evaluation (2002)
Gao, Z.-M.: Automatic identification of English collocation errors based on dependency relations. In: 27th Pacific Asia Conference on Language, Information, and Computation, pp. 550–555 (2013)
Wu, J.-C., Chang, Y.-C., Mitamura, T., Chang, J.S.: Automatic collocation suggestion in academic writing. In: Proceedings of the ACL 2010 Conference, pp. 115–119 (2010)
Davies, M.: The Corpus of Contemporary American English: 450 million words, 1990–present (2008). http://corpus.byu.edu/coca/
Jian, J.-Y., Chang, Y.-C., Chang, J.S.: TANGO: bilingual collocational concordancer. In: Annual Conference of the Association for Computational Linguistics (2004)
Ackermann, K., Chen, Y.-H.: Developing the Academic Collocation List (ACL) – a corpus driven and expert-judged approach. J. Engl. Acad. Purp. 12(4), 235–247 (2013)
Bahns, J.: Lexical collocations: a contrastive view. ELT J. 47(1), 56–63 (1993)
Peter, H.: Phraseology and second language proficiency. Appl. Linguist. 19(1), 24–44 (1998)
Smadja, F.: From n-grams to collocations an evaluation of Xtract. In: Proceedings of the 29th Annual Meeting on Association for Computational Linguistics (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Chu, YL., Wang, TI. (2018). A Sentence-Wide Collocation Recommendation System with Error Detection for Academic Writing. In: Wu, TT., Huang, YM., Shadiev, R., Lin, L., Starčič, A. (eds) Innovative Technologies and Learning. ICITL 2018. Lecture Notes in Computer Science(), vol 11003. Springer, Cham. https://doi.org/10.1007/978-3-319-99737-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-99737-7_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99736-0
Online ISBN: 978-3-319-99737-7
eBook Packages: Computer ScienceComputer Science (R0)