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Computational linguistics is an interdisciplinary field concerned with the computational modelling of natural language, as well as the study of appropriate computational approaches to linguistic questions. In general, computational linguistics draws upon linguistics, computer science, artificial intelligence, mathematics, logic, philosophy, cognitive science, cognitive psychology, psycholinguistics, anthropology and neuroscience, among others.
Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval. It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. To this end, natural language processing often borrows ideas from theoretical linguistics. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
Corpus linguistics is an empirical method for the study of language by way of a text corpus. Corpora are balanced, often stratified collections of authentic, "real world", text of speech or writing that aim to represent a given linguistic variety. Today, corpora are generally machine-readable data collections.
In linguistics and natural language processing, a corpus or text corpus is a dataset, consisting of natively digital and older, digitalized, language resources, either annotated or unannotated.
Head-driven phrase structure grammar (HPSG) is a highly lexicalized, constraint-based grammar developed by Carl Pollard and Ivan Sag. It is a type of phrase structure grammar, as opposed to a dependency grammar, and it is the immediate successor to generalized phrase structure grammar. HPSG draws from other fields such as computer science and uses Ferdinand de Saussure's notion of the sign. It uses a uniform formalism and is organized in a modular way which makes it attractive for natural language processing.
Parsing, syntax analysis, or syntactic analysis is the process of analyzing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar. The term parsing comes from Latin pars (orationis), meaning part.
Linguistic Knowledge Builder (LKB) is a free and open source grammar engineering environment for creating grammars and lexicons of natural languages. Any unification-based grammar can be implemented, but LKB is typically used for grammars with typed feature structures such as HPSG. LKB is free software under the MIT license.
The American National Corpus (ANC) is a text corpus of American English containing 22 million words of written and spoken data produced since 1990. Currently, the ANC includes a range of genres, including emerging genres such as email, tweets, and web data that are not included in earlier corpora such as the British National Corpus. It is annotated for part of speech and lemma, shallow parse, and named entities.
In linguistics, a treebank is a parsed text corpus that annotates syntactic or semantic sentence structure. The construction of parsed corpora in the early 1990s revolutionized computational linguistics, which benefitted from large-scale empirical data.
Computational semantics is the study of how to automate the process of constructing and reasoning with meaning representations of natural language expressions. It consequently plays an important role in natural-language processing and computational linguistics.
Linguistic categories include
The Quranic Arabic Corpus is an annotated linguistic resource consisting of 77,430 words of Quranic Arabic. The project aims to provide morphological and syntactic annotations for researchers wanting to study the language of the Quran.
MedSLT is a medium-ranged open source spoken language translator developed by the University of Geneva. It is funded by the Swiss National Science Foundation. The system has been designed for the medical domain. It currently covers the doctor-patient diagnosis dialogues for the domains of headache, chest and abdominal pain in English, French, Japanese, Spanish, Catalan and Arabic. The vocabulary used ranges from 350 to 1000 words depending on the domain and language pair.
Deep linguistic processing is a natural language processing framework which draws on theoretical and descriptive linguistics. It models language predominantly by way of theoretical syntactic/semantic theory. Deep linguistic processing approaches differ from "shallower" methods in that they yield more expressive and structural representations which directly capture long-distance dependencies and underlying predicate-argument structures.
The knowledge-intensive approach of deep linguistic processing requires considerable computational power, and has in the past sometimes been judged as being intractable. However, research in the early 2000s had made considerable advancement in efficiency of deep processing. Today, efficiency is no longer a major problem for applications using deep linguistic processing.
The following outline is provided as an overview of and topical guide to natural-language processing:
Minimal recursion semantics (MRS) is a framework for computational semantics. It can be implemented in typed feature structure formalisms such as head-driven phrase structure grammar and lexical functional grammar. It is suitable for computational language parsing and natural language generation. MRS enables a simple formulation of the grammatical constraints on lexical and phrasal semantics, including the principles of semantic composition. This technique is used in machine translation.
Emily Menon Bender is an American linguist who is a professor at the University of Washington. She specializes in computational linguistics and natural language processing. She is also the director of the University of Washington's Computational Linguistics Laboratory. She has published several papers on the risks of large language models and on ethics in natural language processing.
Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning. Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation, question answering, ontology induction, automated reasoning, and code generation. The phrase was first used in the 1970s by Yorick Wilks as the basis for machine translation programs working with only semantic representations. Semantic parsing is one of the important tasks in computational linguistics and natural language processing.
Universal Dependencies, frequently abbreviated as UD, is an international cooperative project to create treebanks of the world's languages. These treebanks are openly accessible and available. Core applications are automated text processing in the field of natural language processing (NLP) and research into natural language syntax and grammar, especially within linguistic typology. The project's primary aim is to achieve cross-linguistic consistency of annotation, while still permitting language-specific extensions when necessary. The annotation scheme has it roots in three related projects: Stanford Dependencies, Google universal part-of-speech tags, and the Interset interlingua for morphosyntactic tagsets. The UD annotation scheme uses a representation in the form of dependency trees as opposed to a phrase structure trees. At the present time, there are just over 200 treebanks of more than 100 languages available in the UD inventory.
Syntactic parsing is the automatic analysis of syntactic structure of natural language, especially syntactic relations and labelling spans of constituents. It is motivated by the problem of structural ambiguity in natural language: a sentence can be assigned multiple grammatical parses, so some kind of knowledge beyond computational grammar rules is needed to tell which parse is intended. Syntactic parsing is one of the important tasks in computational linguistics and natural language processing, and has been a subject of research since the mid-20th century with the advent of computers.
References
- ↑ DELPH-IN: Open-Source Deep Processing
- ↑ Ann Copestake, Dan Flickinger, Carl Pollard and Ivan A. Sag. 2005. Minimal Recursion Semantics: An Introduction Archived 2012-07-17 at the Wayback Machine . In Proceedings of Research on Language and Computation.
- ↑ "PET Parser website". Archived from the original on 2022-03-29. Retrieved 2013-07-30.
- ↑ ACE parser/generator homepage
- ↑ Stephan Oepen, Erik Velldal, Jan Tore Lønning, Paul Meurer, Victoria Rosén, and Dan Flickinger. 2007.Towards hybrid quality-oriented machine translation. On linguistics and probabilities in MT Archived 2020-08-06 at the Wayback Machine . In Proceedings of the 10th International Conference on Theoretical and Methodological Issues in Machine Translation, pp.144–153. Skövde, Sweden.
- ↑ DELPH-IN catalog of grammars
- ↑ Fokkens, Antske, Emily M. Bender and Varvara Gracheva. 2012. LinGO Grammar Matrix Customization System Documentation. Online resource.
- ↑ Fokkens, A., Avgustinova, T., and Zhang, Y. 2012. Climb grammars: three projects using metagrammar engineering. In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey.
- ↑ MRS Test Suite page
- ↑ Dan Flickinger, Stephan Oepen, and Gisle Ytrestøl. 2010. WikiWoods: Syntacto-semantic annotation for English Wikipedia. In Proceedings of LREC-2010, pages 1665–1671.
- ↑ Dan Flickinger, Valia Kordoni and Yi Zhang. 2012. DeepBank: A Dynamically Annotated Treebank of the Wall Street Journal Archived 2016-03-04 at the Wayback Machine . In Proceedings of TLT-11, Lisbon, Portugal.
- ↑ DeepBank homepage
- ↑ DELPH-IN CatB page
- ↑ Official Cathedral and the Bazaar webpage
- ↑ DELPH-IN 2013 Summit: Special Interest Group in Useability