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
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
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Related Journals

open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 10.4
SJR: 1.195
SNIP: 3.824
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 2.8
SJR: 0.203
SNIP: 0.959
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 8.3
SJR: 0.96
SNIP: 3.172
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 2.7
SJR: 0.942
SNIP: 1.604

Journal Performance & Insights

Impact Factor

CiteRatio

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.

A measure of average citations received per peer-reviewed paper published in the journal.

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

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

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

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)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

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

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

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

insights Insights

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

Natural Language Engineering

Guideline source: View

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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

i
Last updated on
11 Nov 2021
i
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
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969 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
801 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
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794 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
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679 Citations
Journal Article DOI: 10.1017/S1351324997001502
Building applied natural language generation systems
Ehud Reiter1, Robert Dale2

Abstract:

In this article, we give an overview of Natural Language Generation (NLG) from an applied system-building perspective The article includes a discussion of when NLG techniques should be used; suggestions for carrying out requirements analyses; and a description of the basic NLG tasks of content determination, discourse plannin... In this article, we give an overview of Natural Language Generation (NLG) from an applied system-building perspective The article includes a discussion of when NLG techniques should be used; suggestions for carrying out requirements analyses; and a description of the basic NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation Throughout, the emphasis is on established techniques that can be used to build simple but practical working systems now We also provide pointers to techniques in the literature that are appropriate for more complicated scenarios read more read less

Topics:

Natural language generation (62%)62% related to the paper, Referring expression generation (59%)59% related to the paper, Lexicalization (52%)52% related to the paper
View PDF
663 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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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?

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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

1. Can I write Natural Language Engineering in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Natural Language Engineering guidelines and auto format it.

2. Do you follow the Natural Language Engineering guidelines?

Yes, the template is compliant with the Natural Language Engineering guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Natural Language Engineering?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Natural Language Engineering citation style.

4. Can I use the Natural Language Engineering templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Natural Language Engineering.

5. Can I use a manuscript in Natural Language Engineering that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Natural Language Engineering that you can download at the end.

6. How long does it usually take you to format my papers in Natural Language Engineering?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Natural Language Engineering.

7. Where can I find the template for the Natural Language Engineering?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Natural Language Engineering's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Natural Language Engineering's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Natural Language Engineering an online tool or is there a desktop version?

SciSpace's Natural Language Engineering is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Natural Language Engineering?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Natural Language Engineering?”

11. What is the output that I would get after using Natural Language Engineering?

After writing your paper autoformatting in Natural Language Engineering, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Natural Language Engineering's impact factor high enough that I should try publishing my article there?

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 these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Natural Language Engineering?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Natural Language Engineering. The table below indicates the level of access a journal has as per Sherpa Romeo's 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.

14. What are the most common citation types In Natural Language Engineering?

The 5 most common citation types in order of usage for Natural Language Engineering are:.

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

15. How do I submit my article to the Natural Language Engineering?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Natural Language Engineering's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Natural Language Engineering in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Natural Language Engineering Endnote style according to Elsevier 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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