Computational linguistics has its origins in the post-Second World War research on translation of Russian-language scientific journal articles in the United States. Today, biomedical natural language processing treats clinical data, the scientific literature, and social media, with use cases ranging from studying adverse effects of drugs to interpreting high-throughput genomic assays (N\u00e9v\u00e9ol and Zweigenbaum 2018). Many of the most prominent research areas in the field involve extracting information from text and normalizing it to enormous databases of domain-relevant semantic classes, such as genes, diseases, and biological processes. 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There is a considerable literature on applications of statistical methods in natural-language processing. This chapter focuses on two types of applications: (1) recognition\/transduction applications based on Shannon\u2019s Noisy Channel such as speech recognition, optical character recognition (OCR), spelling correction, part-of-speech (POS) tagging, and machine translation (MT); and (2) discrimination\/ranking applications such as sentiment analysis, information retrieval, spam email filtering, author identification, and word sense disambiguation (WSD). Shannon\u2019s Noisy-Channel model is often used for the first type, and linear separators such as Naive Bayes and logistic regression are often used for the second type. These techniques have produced successful products that are being used by large numbers of people every day: web search, spelling correction, translation, etc. Despite successes such as these, it should be mentioned that all approximations have their limitations. At some point, perhaps in the not-too-distant future, the next generation may discover that the low-hanging fruit has been pretty well picked over, and it may be necessary to revisit some of these classic limitations.<\/p>","DOI":"10.1093\/oxfordhb\/9780199573691.013.54","type":"reference-entry","created":{"date-parts":[[2016,9,14]],"date-time":"2016-09-14T15:27:25Z","timestamp":1473866845000},"source":"Crossref","is-referenced-by-count":0,"title":["Statistical Models for Natural Language Processing"],"prefix":"10.1093","author":[{"given":"Kenneth W.","family":"Church","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2016,8,3]]},"container-title":["The Oxford Handbook of Computational Linguistics 2nd edition"],"original-title":["Statistical Models for Natural Language Processing"],"language":"en","deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T21:55:08Z","timestamp":1659477308000},"score":34.9438,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/edited-volume\/42643\/chapter\/358149731"}},"editor":[{"given":"Ruslan","family":"Mitkov","sequence":"first","affiliation":[]}],"issued":{"date-parts":[[2016,8,3]]},"ISBN":["9780199573691"],"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/oxfordhb\/9780199573691.013.54","published":{"date-parts":[[2016,8,3]]}},{"indexed":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T10:45:44Z","timestamp":1714992344546},"reference-count":0,"publisher":"Oxford University Press","isbn-type":[{"value":"9780199573691","type":"print"}],"content-domain":{"domain":[],"crossmark-restriction":false},"abstract":"
Natural Language Processing (NLP) is a dynamic and rapidly developing field in which new trends, techniques, and applications are constantly emerging. This chapter focuses mainly on recent developments in NLP which could not be covered in other chapters of the Handbook. Topics such as crowdsourcing and processing of large datasets, which are no longer that recent but are widely used and not covered at length in any other chapter, are also presented. The chapter starts by describing how the availability of tools and resources has had a positive impact on the field. The proliferation of user-generated content has led to the emergence of research topics such as sarcasm and irony detection, automatic assessment of user-generated content, and stance detection. All of these topics are discussed in the chapter. The field of NLP is approaching maturity, a fact corroborated by the latest developments in the processing of texts for financial purposes and for helping users with disabilities, two topics that are also discussed here. The chapter presents examples of how researchers have successfully combined research in computer vision and natural language processing to enable the processing of multimodal information, as well as how the latest advances in deep learning have revitalized research on chatbots and conversational agents. The chapter concludes with a comprehensive list of further reading material and additional resources.<\/p>","DOI":"10.1093\/oxfordhb\/9780199573691.013.005","type":"reference-entry","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T17:14:23Z","timestamp":1612977263000},"source":"Crossref","is-referenced-by-count":0,"title":["Recent Developments in Natural Language Processing"],"prefix":"10.1093","author":[{"given":"Constantin","family":"Orasan","sequence":"additional","affiliation":[]},{"given":"Ruslan","family":"Mitkov","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2021,2,10]]},"container-title":["The Oxford Handbook of Computational Linguistics 2nd edition"],"original-title":["Recent Developments in Natural Language Processing"],"language":"en","deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T21:55:24Z","timestamp":1659477324000},"score":34.611603,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/edited-volume\/42643\/chapter\/358154781"}},"editor":[{"given":"Ruslan","family":"Mitkov","sequence":"first","affiliation":[]}],"issued":{"date-parts":[[2021,2,10]]},"ISBN":["9780199573691"],"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/oxfordhb\/9780199573691.013.005","published":{"date-parts":[[2021,2,10]]}},{"indexed":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T05:35:19Z","timestamp":1731476119303,"version":"3.28.0"},"reference-count":0,"publisher":"IEEE","isbn-type":[{"value":"9798350392098","type":"electronic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/clnlp64123.2024","type":"proceedings","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T18:37:16Z","timestamp":1731436636000},"source":"Crossref","is-referenced-by-count":0,"title":["2024 International Conference on Computational Linguistics and Natural Language Processing (CLNLP)"],"prefix":"10.1109","member":"263","event":{"name":"2024 International Conference on Computational Linguistics and Natural Language Processing (CLNLP)","location":"Yinchuan, China","start":{"date-parts":[[2024,7,19]]},"end":{"date-parts":[[2024,7,21]]}},"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T18:37:16Z","timestamp":1731436636000},"score":34.076965,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/xpl\/mostRecentIssue.jsp?punumber=10743122"}},"issued":{"date-parts":[[2024]]},"ISBN":["9798350392098"],"references-count":0,"URL":"https:\/\/doi.org\/10.1109\/clnlp64123.2024","published":{"date-parts":[[2024]]}},{"indexed":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T10:44:23Z","timestamp":1714992263093},"reference-count":0,"publisher":"Oxford University Press","isbn-type":[{"value":"9780199573691","type":"print"}],"content-domain":{"domain":[],"crossmark-restriction":false},"abstract":"
When building applications for educational settings, Natural Language Processing (NLP) researchers should give serious consideration to the educational problem spaces for which an application is intended. This chapter discusses contexts in which NLP applications can support major educational problem spaces. 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