scholarly journals NLP commercialisation in the last 25 years

2019 ◽  
Vol 25 (3) ◽  
pp. 419-426
Author(s):  
Robert Dale
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Practical Application ◽  
Quarter Century ◽  
Language Engineering

AbstractThe Journal of Natural Language Engineering is now in its 25th year. The editorial preface to the first issue emphasised that the focus of the journal was to be on the practical application of natural language processing (NLP) technologies: the time was ripe for a serious publication that helped encourage research ideas to find their way into real products. The commercialisation of NLP technologies had already started by that point, but things have advanced tremendously over the last quarter-century. So, to celebrate the journal’s anniversary, we look at how commercial NLP products have developed over the last 25 years.

scholarly journals Editorial note

2014 ◽  
Vol 21 (1) ◽  
pp. 1-2
Author(s):  
Mitkov Ruslan
Keyword(s):  
Natural Language Processing ◽  
Social Science ◽  
Natural Language ◽  
Language Processing ◽  
Citation Index ◽  
Editorial Note ◽  
High Quality ◽  
Language Engineering ◽  
Thomson Reuters ◽  
The Social

The Journal of Natural Language Engineering (JNLE) has enjoyed another very successful year. Two years after being accepted into Thomson Reuters Citation Index and being indexed in many of their products (including both the Science and the Social Science editions of the Journals Citation Rankings (JCR)), the journal further established itself as a leading forum for high-quality articles covering all aspects of Natural Language Processing research, including, but not limited to, the engineering of natural language methods and applications. I am delighted to report an increased number of submissions reaching a total of 92 between January–September 2014.

scholarly journals Editorial Note

2010 ◽  
Vol 16 (1) ◽  
pp. 1-2
Author(s):  
Ruslan Mitkov
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Industrial Applications ◽  
Editorial Note ◽  
Original Research ◽  
Language Engineering ◽  
Practical Applications ◽  
Market Opportunities

Natural Language Engineering (NLE) enters the second decade of the twenty-first century having established itself as a leading forum for high-quality articles covering all aspects of applied Natural Language Processing research, including, but not limited to, the engineering of natural language methods and applications. It continues to promote first class original research and bridge the gap between traditional computational linguistics research and the implementation of practical applications with potential real-world use. The journal has responded in several ways to the ongoing interest in and growth of research in this area. In 2007 NLE increased its number of pages per issue, thus enabling the publication of more articles. As of January 2010, new publication types are also promoted. In addition to welcoming articles which report on original, unpublished research, the journal now invites surveys presenting the state of the art in important areas of Natural Language Engineering and Natural Language Processing (such as tasks, tools, resources or applications) as well as squibs discussing specific problems. Book reviews and reports on industrial applications will continue to have a prominent place in the Journal. Conference reports, comparative discussions of Natural Language Engineering products and policy-orientated papers examining, for example, funding programmes or market opportunities, are welcome too. Special issues will remain an important feature of the Journal. We envisage one special issue per year, on average. Special issues are selected on a competitive basis after regular calls for proposals.

scholarly journals Editorial Note

2011 ◽  
Vol 18 (1) ◽  
pp. i-i
Author(s):  
Ruslan Mitkov
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Research Community ◽  
Editorial Note ◽  
Language Engineering ◽  
Wide Range ◽  
To Receive

Natural Language Engineering (NLE) has enjoyed another year promoting research of applied Natural Language Processing and serving the research community in the field. We were particularly pleased to register an increasing number of submissions on a wide range of topics reflecting the growing importance of the field. We were also delighted to receive a number of submissions representing the new article types announced in 2010, such as surveys and squibs.

scholarly journals Trends In Natural Language Processing : Scope And Challenges

2021 ◽  
pp. 393-401
Author(s):  
Sagarmoy Ganguly ◽  
Asoke Nath
Keyword(s):  
Quantum Mechanics ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Quantum Cryptography ◽  
Language Processing ◽  
Quantum Key Distribution ◽  
Quantum Physics ◽  
Secure Communications ◽  
Practical Application

Quantum cryptography is a comparatively new and special type of cryptography which uses Quantum mechanics to provide unreal protection of data/information and unconditionally secure communications. This is achieved with Quantum Key Distribution (QKD) protocols which is a representation of an essential practical application of Quantum Computation. In this paper the authors will venture the concept of QKD by reviewinghow QKD works, the authors shall take a look at few protocols of QKD, followed by a practical example of Quantum Cryptography using QKD and certain limitations from the perspective of Computer Science in specific and Quantum Physics in general.

Finite state methods in natural language processing

2003 ◽  
Vol 9 (1) ◽  
pp. 1-3 ◽  
Author(s):  
LAURI KARTTUNEN ◽  
KIMMO KOSKENNIEMI ◽  
GERTJAN VAN NOORD
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Summer School ◽  
Special Issue ◽  
Language Engineering ◽  
Finite State ◽  
State Models ◽  
Finite State Models

Finite state methods have been in common use in various areas of natural language processing (NLP) for many years. A series of specialized workshops in this area illustrates this. In 1996, András Kornai organized a very successful workshop entitled Extended Finite State Models of Language. One of the results of that workshop was a special issue of Natural Language Engineering (Volume 2, Number 4). In 1998, Kemal Oflazer organized a workshop called Finite State Methods in Natural Language Processing. A selection of submissions for this workshop were later included in a special issue of Computational Linguistics (Volume 26, Number 1). Inspired by these events, Lauri Karttunen, Kimmo Koskenniemi and Gertjan van Noord took the initiative for a workshop on finite state methods in NLP in Helsinki, as part of the European Summer School in Language, Logic and Information. As a related special event, the 20th anniversary of two-level morphology was celebrated. The appreciation of these events led us to believe that once again it should be possible, with some additional submissions, to compose an interesting special issue of this journal.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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