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Anniversary article: Then and now: 25 years of progress in natural language engineering

Published online by Cambridge University Press:  15 May 2019

John Tait  and
Yorick Wilks
John Tait*
Affiliation:
Johntait.net Ltd, Thorpe Thewles, Stockton-on-Tees, UK
Yorick Wilks
Affiliation:
Florida Institute of Human and Machine, Cognition 15, SE Osceola, Ocala FL 34471, USA
*
*Corresponding author. Email: john@johntait.net
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Abstract

The paper reviews the state of the art of natural language engineering (NLE) around 1995, when this journal first appeared, and makes a critical comparison with the current state of the art in 2018, as we prepare the 25th Volume. Specifically the then state of the art in parsing, information extraction, chatbots, and dialogue systems, speech processing and machine translation are briefly reviewed. The emergence in the 1980s and 1990s of machine learning (ML) and statistical methods (SM) is noted. Important trends and areas of progress in the subsequent years are identified. In particular, the move to the use of n-grams or skip grams and/or chunking with part of speech tagging and away from whole sentence parsing is noted, as is the increasing dominance of SM and ML. Some outstanding issues which merit further research are briefly pointed out, including metaphor processing and the ethical implications of NLE.

Keywords

Information extractionMachine learningMachine translationParsingStatistical methods

Information

Type
Article
Information
Natural Language Engineering , Volume 25 , Issue 3 , May 2019 , pp. 405 - 418
Copyright
© Cambridge University Press 2019 

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References

Andor, D. Alberti, C., Weiss, D., Severyn, A., Presta, A., Ganchev, K., Petrov, S. and Collins, M. (2016). Globally Normalized Transition-Based Neural Networks. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany. 2442–2452.Google Scholar
Azmi, A. and Alshenaifi, N. (2017). Lemaza: An Arabic why-question answering system. Natural Language Engineering 23(6), 877–903. doi: 10.1017/S1351324917000304CrossRefGoogle Scholar
Bachenko, J., Fitzpatrick, E. and Daugherty, J. (1995). A rule-based phrase parser for real-time text-to-speech synthesis. Natural Language Engineering 1(2), 191–212. doi: 10.1017/S1351324900000140CrossRefGoogle Scholar
Ballim, A. and Wilks, Y. (1991/2018). Artificial Believers: The Ascription of Belief. New Jersey: Ablex Books; reprinted by Routledge, London.Google Scholar
Banea, C. and Mihalcea, R. (2018). Possession identification in text. Natural Language Engineering 24(4), 589–610. doi: 10.1017/S1351324918000062CrossRefGoogle Scholar
Biemann, C., Faralli, S., Panchenko, A. and Ponzetto, S. (2018). A framework for enriching lexical semantic resources with distributional semantics. Natural Language Engineering 24(2), 265–312. doi: 10.1017/S135132491700047XCrossRefGoogle Scholar
Boguraev, B. and Briscoe, T. (Eds) (1989). Computional Lexicography for Natural Language Processing. Harlow, Essex, England: Longman.Google Scholar
Boguraev, B.K., Garigliano, R. and Tait, J.I. (1995). Editorial. Natural Language Engineering 1(1), 1–7.Google Scholar
Boguraev, B., Carroll, J., Briscoe, E., Carter, D. and Grover, C. (1987). The Derivation of a Grammatically-Indexed Lexicon from the Longman Dictionary of Contemporary English. In Proceedings of the 25th Annual Meeting of the Association for Computational Linguistics, Stanford, CA. 193–200.CrossRefGoogle Scholar
Bond, F. and Paik, K. (2012). A survey of wordnets and their licenses. In Proceedings of the 6th Global WordNet Conference (GWC 2012). Matsue. 64–71Google Scholar
Braun, D., Reiter, E. and Siddharthan, A. (2018). SaferDrive: An NLG-based behaviour change support system for drivers. Natural Language Engineering 24(4), 551–588. doi: 10.1017/S1351324918000050CrossRefGoogle Scholar
Brown, J.C. (1995). High speed feature unification and parsing. Natural Language Engineering 1(4), 309–338.CrossRefGoogle Scholar
Callison-Burch, C., Osborne, M., Koehn, P. (2006). Re-evaluation the Role of Bleu in Machine Translation Research. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006). Trento, Italy. 249–256.Google Scholar
Chelba, C. and Jelinek, F. (2000) Structured language modeling. Computer Speech & Language 14(4), 283–332. doi: 10.1006/csla.2000.0147CrossRefGoogle Scholar
Chen, Y., Zheng, Q., Tian, F., Liu, H., Hao, Y. and Shah, N. (2018). Exploring open information via event network. Natural Language Engineering 24(2), 199–220. doi: 10.1017/S1351324917000390CrossRefGoogle Scholar
Cho, K. (2018). Deep learning. In Mitkov, R. (ed), The Oxford Handbook of Computational Linguistics, 2nd Edition. Oxford, England: Oxford University Press. doi: 10.1093/oxfordhb/9780199573691.013.55Google Scholar
Choi, E., Seo, M., Chen, D., Jia, R. and Berant, J. (2018). Proceedings of the Workshop on Machine Reading for Question Answering. Melbourne, Australia: Association for Computational Linguistics.Google Scholar
Church, K.W. and Gale, W.A. (1995). Poisson mixtures. Natural Language Engineering 1(4), 163–190.CrossRefGoogle Scholar
Colby, K.M. (1973). Simulation of Belief Systems. In Schank, R.C. and Colby, K.M. (eds), Computer Models of Thought and Language. San Francisco: W.H. Freeman and Co. 251–286.Google Scholar
Cranias, L., Papageorgiou, H. and Piperidis, S. (1997). Example retrieval from a translation memory. Natural Language Engineering 3(4), 255–277CrossRefGoogle Scholar
Cunningham, H. (1999). A definition and short history of language engineering. Natural Language Engineering 5(1), 1–16.CrossRefGoogle Scholar
De Jong, G.F. (1982). An overview of the FRUMP system. In Lehnert, W.G. and Ringle, M.H. (eds), Strategies for Natural Language Processing. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Derici, C., Aydin, Y., Yenialaca, Ç, Aydin, N., Kartal, G., Özgür, A. and Güngör, T. (2018). A closed-domain question answering framework using reliable resources to assist students. Natural Language Engineering 24(5), 725–762. doi: 10.1017/S1351324918000141CrossRefGoogle Scholar
Evans, R., Gaizauskas, R., Cahill, L.J., Walker, J., Richardson, J. and Dixon, A. (1995). POETIC: A system for gathering and disseminating traffic information. Natural Language Engineering 1(4), 363–387.CrossRefGoogle Scholar
Fatima, M., Anwar, S., Naveed, A., Arshad, W., Nawab, R., Iqbal, M. and Masood, A. (2018). Multilingual SMS-based author profiling: Data and methods. Natural Language Engineering 24(5), 695–724. doi: 10.1017/S1351324918000244CrossRefGoogle Scholar
Fellbaum, C. and Miller, G.A. (1998). Wordnet: An Electronic Lexical Database. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Floridi, L., Taddeo, M. and Turilli, M. (2009). Turing’s imitation game: Still an impossible challenge for all machines and some judges—an evaluation of the 2008 Loebner contest. Minds & Machines (19):145–150. doi: 10.1007/s11023-008-9130-6.CrossRefGoogle Scholar
Friedman, C., Hripcsak, G., DuMouchel, W., Johnson, S.B. and Clayton, P.D. (1995). Natural language processing in an operational clinical information system. Natural Language Engineering 1(1), 83–108.CrossRefGoogle Scholar
Garcia, M., Gómez-Rodríguez, C. and Alonso, M. (2018). New treebank or repurposed? On the feasibility of cross-lingual parsing of Romance languages with Universal dependencies. Natural Language Engineering 24(1), 91–122. doi: 10.1017/S1351324917000377CrossRefGoogle Scholar
Garside, R. (1987). The CLAWS Word-tagging System. In Garside, R., Leech, G. and Sampson, G. (eds), The Computational Analysis of English: A Corpus-Based Approach. London: Longman.Google Scholar
Giannella, C., Winder, R. and Petersen, S. (2017). Dropped personal pronoun recovery in Chinese SMS. Natural Language Engineering 23(6), 905–927. doi: 10.1017/S1351324917000158CrossRefGoogle Scholar
Grishman, R. and Sundheim, B. (1996). Message Understanding Conference - 6: A Brief History. In Proceedings of the 16th International Conference on Computational Linguistics (COLING), I, Copenhagen, 466–471.CrossRefGoogle Scholar
Gründer-Fahrer, S., Schlaf, A., Wiedemann, G. and Heyer, G. (2018). Topics and topical phases in German social media communication during a disaster. Natural Language Engineering 24(2), 221–264. doi: 10.1017/S1351324918000025CrossRefGoogle Scholar
Han, Y.S. and Choi, K.-S. (1995). Best parse parsing with Earley’s and Inside algorithms on probabilistic RTN. Natural Language Engineering 1(2), 147–161.CrossRefGoogle Scholar
Hirano, D., Tanaka-Ishii, K. and Finch, A. (2018). Extraction of templates from phrases using Sequence Binary Decision Diagrams. Natural Language Engineering 24(5), 763–795. doi: 10.1017/S1351324918000268CrossRefGoogle Scholar
Hutchins, J. and Somers, H. (1992). An Introduction to Machine Translation. London: Academic Press.Google Scholar
Juang, B.H. and Rabiner, L.R. (2005). Automatic Speech Recognition– A Brief History of the Technology Development. Georgia Institute of Technology, Atlanta. https://web.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/354_LALI-ASRHistory-final-10-8.pdf (Checked 10 December 2018)Google Scholar
Justeson, J. and Katz, S. (1995). Technical terminology: Some linguistic properties and an algorithm for identification in text. Natural Language Engineering 1(1), 9–27. doi: 10.1017/S1351324900000048CrossRefGoogle Scholar
Kadari, R., Zhang, Y., Zhang, W. and Liu, T. (2018). CCG supertagging with bidirectional long short-term memory networks. Natural Language Engineering 24(1), 77–90. doi: 10.1017/S1351324917000250CrossRefGoogle Scholar
Krüger, K., Lukowiak, A., Sonntag, J., Warzecha, S. and Stede, M. (2017). Classifying news versus opinions in newspapers: Linguistic features for domain independence. Natural Language Engineering 23(5), 687–707. doi: 10.1017/S1351324917000043CrossRefGoogle Scholar
Kübler, S., Liu, C. and Sayyed, Z. (2018). To use or not to use: Feature selection for sentiment analysis of highly imbalanced data. Natural Language Engineering 24(1), 3–37. doi: 10.1017/S1351324917000298CrossRefGoogle Scholar
Laddha, A. and Mukherjee, A. (2018). Aspect opinion expression and rating prediction via LDA-CRF hybrid. Natural Language Engineering 24(4), 611–639. doi: 10.1017/S135132491800013XCrossRefGoogle Scholar
Langlois, D., Saad, M. and Smaliki, K. (2018). Alignment of comparable documents: Comparison of similarity measures on French–English–Arabic data. Natural Language Engineering 24(5), 677–694. doi: 10.1017/S1351324918000232CrossRefGoogle Scholar
Läubli, S. and Orrego-Carmona, D. (2017). When Google Translate is better than Some Human Colleagues, those People are no longer Colleagues. In Proceedings of Translation and the Computer 39, Asling, the International Association for Advancement in Language Technology, London. 59–69.Google Scholar
Li, B., Gaussier, E. and Yang, D. (2018). Measuring bilingual corpus comparability. Natural Language Engineering 24(4), 523–549. doi: 10.1017/S1351324917000481CrossRefGoogle Scholar
MacKay, D.J.C. and Bauman Peto, L.C. (1995). A hierarchical Dirichlet language model. Natural Language Engineering 1(3), 289–307.CrossRefGoogle Scholar
Manning, C.D. (2015). Computational linguistics and deep learning. Computational Linguistics 41(4), 701–707.CrossRefGoogle Scholar
Marcus, M.P., Marcinkiewicz, M.A. and Santorini, B. (1993) Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics 19(2), 313–330.Google Scholar
Marrero, M. and Urbano, J. (2018). A semi-automatic and low-cost method to learn patterns for named entity recognition. Natural Language Engineering 24(1), 39–75. doi: 10.1017/S135132491700016XCrossRefGoogle Scholar
Michiels, A. (1983). Automatic analysis of texts. In Jones, K.P. (ed), Informatics 7: Intelligent Information Retrieval. Cambridge: Aslib, pp. 103–120.Google Scholar
Mikheev, A. and Liubushkina, L. (1995). Russian morphology: An engineering approach. Natural Language Engineering 1(3), 235–260. doi: 10.1017/S135132490000019XCrossRefGoogle Scholar
Nagao, M. (1984). A framework of a mechanical translation between Japanese and English by analogy principle. In Elithorn, A. and Banerji, R. (eds), Artificial and Human Intelligence. Edited Review Papers Presented at the International NATO Symposium on Artificial and Human Intelligence, 1981. Lyon, Amsterdam, New York, Oxford, North Holland, pp. 173–180.Google Scholar
Oakley, B. (1993). EUROTRA final Review Panel Report. Commission of the European Communities. Available from: http://aei.pitt.edu/36888/1/A2903.pdf (Checked 26 January 2019).Google Scholar
Palmer, M. and Finin, T. (1990). Workshop on the evaluation of natural language processing systems. Computational Linguistics 16(3), 175–181.Google Scholar
Papenini, K., Rouskos, S., Ward, T. and Whu, W.-J. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia. 311–318.Google Scholar
Periñan-Pascual, C. (2018). DEXTER: A workbench for automatic term extraction with specialized corpora. Natural Language Engineering 24(2), 163–198. doi: 10.1017/S1351324917000365CrossRefGoogle Scholar
Pierce, J.R., Carroll, J.B., Hamp, E.P., Hays, D.G., Hockett, C.F., Oettinger, A.G. and Perlis, A. (1966). Language and Machines — Computers in Translation and Linguistics. Washington, DC: ALPAC report, National Academy of Sciences, National Research Council.Google Scholar
Prince, V. and Pernel, D. (1995). Several knowledge models and a blackboard memory for human-machine robust dialogues. Natural Language Engineering 1(20), 113–145.CrossRefGoogle Scholar
Proctor, P. (ed.) (1978). Longman Dictionary of Contemporary English. Harlow, Essex: Longman Group.Google Scholar
Pulman, S. (1995). Anaphora and ellipsis in artificial languages. Natural Language Engineering 1(3), 217–234. doi: 10.1017/S1351324900000188CrossRefGoogle Scholar
Rosenbaum, R. and Lochak, D. (1966). The IBM core grammar of English. In Lieberman, D. (ed), Specification and Utilization of a Transformational Grammar. AFCRL-66-270 (1966). Yorktown Heights, New York: Thomas J. Watson Research Center, IBM Corporation.CrossRefGoogle Scholar
Schank, R.C. and Colby, K.M. (Eds.) (1973). Computer Models of Thought and Language. San Francisco: W.H. Freeman and Co.Google Scholar
Somers, H. (2003). Translation memory. In Somers, H. (ed), Computers and Translation: A Translator’s Guide. Amsterdam: John Benjamins.CrossRefGoogle Scholar
Sparck Jones, K. (1986). Synonymy and Semantic Classification. Edinburgh: Edinburgh University Press.Google Scholar
Sparck Jones, K. and Galliers, J.R. (1995). Evaluating Natural Language Processing Systems: An Analysis and Review. Berlin: Springer.CrossRefGoogle Scholar
Tait, J. (2019). Editorial. Natural Language Engineering 25(1), 1–4.CrossRefGoogle Scholar
Tait, J.I. (ed). (2005). Charting a New Course: Natural Language Processing and Information Retrieval. Dordrecht, NL: Springer.CrossRefGoogle Scholar
Thompson, H. (1983). Natural language processing: A critical analysis of the structure of the field, with some implications for parsing. In Sparck Jones, K. and Wilks, Y. (eds), Automatic Natural Language Parsing. Chichester, England: Ellis Horwood.Google Scholar
Wei, Y., Wei, J. and Yang, Z. (2018). Unsupervised learning of semantic representation for documents with the law of total probability. Natural Language Engineering 24(4), 491–522. doi: 10.1017/S1351324917000420CrossRefGoogle Scholar
Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM 9, 36–45. doi: 10.1145/365153.365168.CrossRefGoogle Scholar
Wilks, Y. (1967). Text searching with templates. Cambridge language research unit, research memorandum. In Ahmad, K., Brewster, C., Stevenson, M. (eds), Words and Intelligence I. Text, Speech and Language Technology, vol. 35. Dordrecht: Springer. Reprinted (2007).Google Scholar
Wilks, Y.A., Slator, B.M. and Guthrie, L.M. (1996). Electric Words. Cambridge, Mass: MIT Press.CrossRefGoogle Scholar
Wilks, Y.A. and Tait, J.I. (2005). A retrospective view of synonymy and semantic classification. In Charting a New Course: Natural Language Processing and Information Retrieval, pp. 1–11. Springer, Dordrecht.Google Scholar
Winograd, T. (1973). A procedural model of language understanding. In Schank, R.C. and Colby, K.M. (eds), (1973). Computer Models of Thought and Language. San Francisco: W.H. Freeman and Co. pp. 152–186.Google Scholar
Wintner, S. and Ornan, U. (1995). Syntactic analysis of Hebrew sentences. Natural Language Engineering 1(3), 261–288. doi: 10.1017/S1351324900000206CrossRefGoogle Scholar