Alexander Clark, Chris Fox and Shalom Lappin (eds): Handbook of computational linguistics and natural language processing

2012 ◽  
Vol 26 (3) ◽  
pp. 281-287
Author(s):  
Pratyush Banerjee
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing

Finite-State Technology

2018 ◽  
Author(s):  
Mans Hulden
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Finite State Machines ◽  
Regular Languages ◽  
Finite State Automata ◽  
State Machines ◽  
Computational Phonology ◽  
Finite State

Finite-state machines—automata and transducers—are ubiquitous in natural-language processing and computational linguistics. This chapter introduces the fundamentals of finite-state automata and transducers, both probabilistic and non-probabilistic, illustrating the technology with example applications and common usage. It also covers the construction of transducers, which correspond to regular relations, and automata, which correspond to regular languages. The technologies introduced are widely employed in natural language processing, computational phonology and morphology in particular, and this is illustrated through common practical use cases.

Advances in Computational Linguistics and Text Processing Frameworks

2020 ◽  
pp. 217-244
Author(s):  
Ayush Srivastav ◽  
Hera Khan ◽  
Amit Kumar Mishra
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

Natural Language Processing

1996 ◽  
Vol 16 ◽  
pp. 70-85 ◽  
Author(s):  
Thomas C. Rindflesch
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Domain Knowledge ◽  
The State ◽  
Point Of View ◽  
Computer Applications ◽  
Significant Progress ◽  
Future Directions

Work in computational linguistics began very soon after the development of the first computers (Booth, Brandwood and Cleave 1958), yet in the intervening four decades there has been a pervasive feeling that progress in computer understanding of natural language has not been commensurate with progress in other computer applications. Recently, a number of prominent researchers in natural language processing met to assess the state of the discipline and discuss future directions (Bates and Weischedel 1993). The consensus of this meeting was that increased attention to large amounts of lexical and domain knowledge was essential for significant progress, and current research efforts in the field reflect this point of view.

scholarly journals MorphoBr: an open source large-coverage full-form lexicon for morphological analysis of Portuguese

2018 ◽  
Vol 11 (3) ◽  
pp. 1-25
Author(s):  
Leonel Figueiredo de Alencar ◽  
Bruno Cuconato ◽  
Alexandre Rademaker
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Open Source ◽  
Computational Linguistics ◽  
Language Processing ◽  
Morphological Analysis ◽  
Computational Techniques ◽  
Processing Technologies ◽  
Finite State ◽  
Full Form

ABSTRACT: One of the prerequisites for many natural language processing technologies is the availability of large lexical resources. This paper reports on MorphoBr, an ongoing project aiming at building a comprehensive full-form lexicon for morphological analysis of Portuguese. A first version of the resource is already freely available online under an open source, free software license. MorphoBr combines analogous free resources, correcting several thousand errors and gaps, and systematically adding new entries. In comparison to the integrated resources, lexical entries in MorphoBr follow a more user-friendly format, which can be straightforwardly compiled into finite-state transducers for morphological analysis, e.g. in the context of syntactic parsing with a grammar in the LFG formalism using the XLE system. MorphoBr results from a combination of computational techniques. Errors and the more obvious gaps in the integrated resources were automatically corrected with scripts. However, MorphoBr's main contribution is the expansion in the inventory of nouns and adjectives. This was carried out by systematically modeling diminutive formation in the paradigm of finite-state morphology. This allowed MorphoBr to significantly outperform analogous resources in the coverage of diminutives. The first evaluation results show MorphoBr to be a promising initiative which will directly contribute to the development of more robust natural language processing tools and applications which depend on wide-coverage morphological analysis.KEYWORDS: computational linguistics; natural language processing; morphological analysis; full-form lexicon; diminutive formation. RESUMO: Um dos pré-requisitos para muitas tecnologias de processamento de linguagem natural é a disponibilidade de vastos recursos lexicais. Este artigo trata do MorphoBr, um projeto em desenvolvimento voltado para a construção de um léxico de formas plenas abrangente para a análise morfológica do português. Uma primeira versão do recurso já está disponível gratuitamente on-line sob uma licença de software livre e de código aberto. MorphoBr combina recursos livres análogos, corrigindo vários milhares de erros e lacunas. Em comparação com os recursos integrados, as entradas lexicais do MorphoBr seguem um formato mais amigável, o qual pode ser compilado diretamente em transdutores de estados finitos para análise morfológica, por exemplo, no contexto do parsing sintático com uma gramática no formalismo da LFG usando o sistema XLE. MorphoBr resulta de uma combinação de técnicas computacionais. Erros e lacunas mais óbvias nos recursos integrados foram automaticamente corrigidos com scripts. No entanto, a principal contribuição de MorphoBr é a expansão no inventário de substantivos e adjetivos. Isso foi alcançado pela modelação sistemática da formação de diminutivos no paradigma da morfologia de estados finitos. Isso possibilitou a MorphoBr superar de forma significativa recursos análogos na cobertura de diminutivos. Os primeiros resultados de avaliação mostram que o MorphoBr constitui uma iniciativa promissora que contribuirá de forma direta para conferir robustez a ferramentas e aplicações de processamento de linguagem natural que dependem de análise morfológica de ampla cobertura.PALAVRAS-CHAVE: linguística computacional; processamento de linguagem natural; análise morfológica; léxico de formas plenas; formação de diminutivos.

scholarly journals Finite-state methods and models in natural language processing

2011 ◽  
Vol 17 (2) ◽  
pp. 141-144
Author(s):  
ANSSI YLI-JYRÄ ◽  
ANDRÁS KORNAI ◽  
JACQUES SAKAROVITCH
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Current Issue ◽  
Special Issue ◽  
The Public ◽  
The Past ◽  
Finite State ◽  
Final Selection

For the past two decades, specialised events on finite-state methods have been successful in presenting interesting studies on natural language processing to the public through journals and collections. The FSMNLP workshops have become well-known among researchers and are now the main forum of the Association for Computational Linguistics' (ACL) Special Interest Group on Finite-State Methods (SIGFSM). The current issue on finite-state methods and models in natural language processing was planned in 2008 in this context as a response to a call for special issue proposals. In 2010, the issue received a total of sixteen submissions, some of which were extended and updated versions of workshop papers, and others which were completely new. The final selection, consisting of only seven papers that could fit into one issue, is not fully representative, but complements the prior special issues in a nice way. The selected papers showcase a few areas where finite-state methods have less than obvious and sometimes even groundbreaking relevance to natural language processing (NLP) applications.

Biomedical Computational Linguistics and Natural Language Processing

2021 ◽  
Author(s):  
Kevin Bretonnel Cohen
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Scientific Journal ◽  
The United States ◽  
Russian Language ◽  
Research Areas ◽  
Semantic Classes ◽  
Genomic Assays

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 (Névéol 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. Moving forward, the field is expected to play a significant role in understanding reproducibility in natural language processing.

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