Natural Language Processing and Language learning

Natural language processing (NLP) is concerned with the automated processing of human language. It addresses the analysis and generation of written and spoken language, though speech processing is often regarded as a separate subfield. NLP can be seen as the applied side of computational linguistics, the interdisciplinary field concerned with formal analysis and computational modeling of language at the intersection of linguistics, computer science, and psychology. In terms of the aspects analyzed by NLP, traditionally lexical, morphological, and syntactic aspects of language were the center of attention, but aspects of meaning, discourse, and the relation to the extralinguistic context have become increasingly prominent in the last decade. This entry focuses on showing the relevance, characterizing the techniques, and delineating the uses of NLP for second language learning. It distinguishes two broad uses of NLP related to language learning. On the one hand, NLP can be used to analyze learner language, that is, words, sentences, or texts produced by language learners. This includes the development of NLP techniques for the analysis of learner language by tutoring systems in intelligent computer‐assisted language learning (ICALL), automated scoring in language testing, as well as the analysis and annotation of learner corpora. On the other hand, NLP for the analysis of native language can also play an important role in the language learning context. Applications in this second domain support the search for and the enhanced presentation of reading material for language learners as well as the generation of exercises and tests based on authentic materials.