Example of Natural Language Engineering format
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Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format
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Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format Example of Natural Language Engineering format
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open access Open Access ISSN: 13513249 e-ISSN: 14698110
recommended Recommended

Natural Language Engineering — Template for authors

Categories Rank Trend in last 3 yrs
Language and Linguistics #40 of 879 up up by 20 ranks
Linguistics and Language #45 of 935 up up by 18 ranks
Artificial Intelligence #99 of 227 down down by 18 ranks
Software #172 of 389 up up by 9 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 140 Published Papers | 537 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 11/11/2021
Insights & related journals
General info
Top papers
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

1.465

30% from 2018

Impact factor for Natural Language Engineering from 2016 - 2019
Year Value
2019 1.465
2018 1.13
2017 0.8
2016 1.065
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 30% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

3.8

6% from 2019

CiteRatio for Natural Language Engineering from 2016 - 2020
Year Value
2020 3.8
2019 3.6
2018 2.8
2017 2.7
2016 2.5
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 6% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

0.29

46% from 2019

SJR for Natural Language Engineering from 2016 - 2020
Year Value
2020 0.29
2019 0.539
2018 0.315
2017 0.264
2016 0.301
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 46% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

1.153

33% from 2019

SNIP for Natural Language Engineering from 2016 - 2020
Year Value
2020 1.153
2019 1.715
2018 1.689
2017 1.344
2016 0.955
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has decreased by 33% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

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CiteRatio: 10.4 | SJR: 1.195 | SNIP: 3.824
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CiteRatio: 2.8 | SJR: 0.203 | SNIP: 0.959
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Natural Language Engineering

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Cambridge University Press

Natural Language Engineering

Natural Language Engineering meets the needs of professionals and researchers working in all areas of computerised language processing, whether from the perspective of theoretical or descriptive linguistics, lexicology, computer science or engineering. Its aim is to bridge the...... Read More

Language and Linguistics

Linguistics and Language

Software

Artificial Intelligence

Arts and Humanities

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Last updated on
11 Nov 2021
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ISSN
1351-3249
i
Impact Factor
High - 1.126
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
unsrt
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Citation Type
Author Year
i
Bibliography Example
Blonder, G. E., Tinkham, M., & Klapwijk, T. M. (1982). Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B, 25(7), 4515–4532.

Top papers written in this journal

Journal Article DOI: 10.1017/S1351324904003523
UIMA: an architectural approach to unstructured information processing in the corporate research environment
David A. Ferrucci1, Adam Lally1
IBM1

Abstract:

IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification a... IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. An analysis of these activities strongly suggested that improving the organization's ability to quickly discover each other's results and rapidly combine different technologies and approaches would accelerate scientific advance. Furthermore, the ability to reuse and combine results through a common architecture and a robust software framework would accelerate the transfer of research results in NLP into IBM's product platforms. Market analyses indicating a growing need to process unstructured information, specifically multilingual, natural language text, coupled with IBM Research's investment in NLP, led to the development of middleware architecture for processing unstructured information dubbed UIMA. At the heart of UIMA are powerful search capabilities and a data-driven framework for the development, composition and distributed deployment of analysis engines. In this paper we give a general introduction to UIMA focusing on the design points of its analysis engine architecture and we discuss how UIMA is helping to accelerate research and technology transfer. read more read less

Topics:

Question answering (54%)54% related to the paper, IBM (52%)52% related to the paper, Machine translation (52%)52% related to the paper, Information management (51%)51% related to the paper
View PDF
936 Citations
Journal Article DOI: 10.1017/S1351324906004505
MaltParser: A language-independent system for data-driven dependency parsing

Abstract:

Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible ma... Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data. read more read less

Topics:

Bottom-up parsing (67%)67% related to the paper, Top-down parsing (67%)67% related to the paper, Parser combinator (65%)65% related to the paper, Parsing (62%)62% related to the paper, Treebank (51%)51% related to the paper
View PDF
797 Citations
Journal Article DOI: 10.1017/S1351324900000048
Technical terminology: some linguistic properties and an algorithm for identification in text
John S. Justeson1, Slava M. Katz2

Abstract:

This paper identifies some linguistic properties of technical terminology, and uses them to formulate an algorithm for identifying technical terms in running text. The grammatical properties discussed are preferred phrase structures: technical terms consist mostly of noun phrases containing adjectives, nouns, and occasionally... This paper identifies some linguistic properties of technical terminology, and uses them to formulate an algorithm for identifying technical terms in running text. The grammatical properties discussed are preferred phrase structures: technical terms consist mostly of noun phrases containing adjectives, nouns, and occasionally prepositions; rerely do terms contain verbs, adverbs, or conjunctions. The discourse properties are patterns of repetition that distinguish noun phrases that are technical terms, especially those multi-word phrases that constitute a substantial majority of all technical vocabulary, from other types of noun phrase.The paper presents a terminology indentification algorithm that is motivated by these linguistic properties. An implementation of the algorithm is described; it recovers a high proportion of the technical terms in a text, and a high proportaion of the recovered strings are vaild technical terms. The algorithm proves to be effective regardless of the domain of the text to which it is applied. read more read less

Topics:

Technical definition (64%)64% related to the paper, Noun phrase (62%)62% related to the paper, Noun (59%)59% related to the paper, Terminology extraction (57%)57% related to the paper, Phrase (56%)56% related to the paper
View PDF
775 Citations
open accessOpen access Journal Article DOI: 10.1017/S135132490000005X
Natural language interfaces to databases-An introduction
Ion Androutsopoulos1, Graeme Ritchie1, Peter Thanisch1

Abstract:

This paper is an introduction to natural language interfaces to databases (NLIDBS). A brief overview of the history of NLIDBS is first given. Some advantages and disadvantages of NLIDBS are then discussed, comparing NLIDBS to formal query languages, form-based interfaces, and graphical interfaces. An introduction to some of t... This paper is an introduction to natural language interfaces to databases (NLIDBS). A brief overview of the history of NLIDBS is first given. Some advantages and disadvantages of NLIDBS are then discussed, comparing NLIDBS to formal query languages, form-based interfaces, and graphical interfaces. An introduction to some of the linguistic problems NLIDBS have to confront follows, for the benefit of readers less familiar with computational linguistics. The discussion then moves on to NLIDB architectures, portability issues, restricted natural language input systems (including menu-based NLIDBS), and NLIDBS with reasoning capabilities. Some less explored areas of NLIDB research are then presented, namely database updates, meta-knowledge questions, temporal questions, and multi-modal NLIDBS. The paper ends with reflections on the current state of the art. read more read less

Topics:

Natural language (50%)50% related to the paper
View PDF
645 Citations
Journal Article DOI: 10.1017/S135132490400364X
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Naiwen Xue1, Fei Xia1, Fu-Dong Chiou1, Marta Palmer1

Abstract:

With growing interest in Chinese Language Processing, numerous NLP tools (e.g., word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on corpora with different segmentation c... With growing interest in Chinese Language Processing, numerous NLP tools (e.g., word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on corpora with different segmentation criteria, part-of-speech tagsets and bracketing guidelines, and therefore, comparisons are difficult. As a first step towards addressing this issue, we have been preparing a large bracketed corpus since late 1998. The first two installments of the corpus, 250 thousand words of data, fully segmented, POS-tagged and syntactically bracketed, have been released to the public via LDC (www.ldc.upenn.edu). In this paper, we discuss several Chinese linguistic issues and their implications for our treebanking efforts and how we address these issues when developing our annotation guidelines. We also describe our engineering strategies to improve speed while ensuring annotation quality. read more read less

Topics:

Treebank (58%)58% related to the paper
View PDF
612 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

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(Before submission check for plagiarism via Turnitin)

clock Less than 3 minutes

What to expect from SciSpace?

Speed and accuracy over MS Word

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With SciSpace, you do not need a word template for Natural Language Engineering.

It automatically formats your research paper to Cambridge University Press formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

Turnitin Stats
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One editor, 100K journal formats – world's largest collection of journal templates

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Easy support from all your favorite tools

Natural Language Engineering format uses unsrt citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Natural Language Engineering guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Natural Language Engineering citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Natural Language Engineering's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

To be honest, the answer is NO. The impact factor is one of the many elements that determine the quality of a journal. Few of those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo Archiving Policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

The 5 most common citation types in order of usage are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for Natural Language Engineering.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Natural Language Engineering Endnote style, according to cambridge-university-press guidelines.

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Typset automatically formats your research paper to Natural Language Engineering formatting guidelines and citation style.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

Andreas Frutiger
Researcher & Ex MS Word user
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