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Improving writing through corpora: Data-driven learning?

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Improving writing through corpora: Data-driven learning?

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UQx

About This Course

Welcome to UQ SLATx 'Improving writing through corpora'!
This short online course aims to provide you with the tools, knowledge and skills to become a 'language detective', using special software to improve your academic writing. As a novice writer for your discipline, you may make mistakes with your writing that you don't know how to solve. If you are a second-language user of English, this can be even more difficult. But what if there was a data-driven solution to your language problems? It just so happens that there is such a solution... The use of CORPORA, or large, principled collections of electronically searchable text - has been proven to really help writers understand how language is actually used across a wide range of contexts. Advances in technology have now made it much easier to get online access to this kind of data, numbering in the millions (or even billions) of words. Better still, we can search for examples of language in use across different disciplines, like engineering, the arts, physics, and so on, ensuring we can use the right terminology for the subjects we are writing. By completing this course, you will be an expert at manipulating millions of words of academic writing data to provide all the answers you need to many problems you might be having with your writing, and boost your knowledge of academic words and phrases to improve your vocabulary and written fluency. You will also receive a certificate of completion for completing all the activities on this course.

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Improving writing through corpora: Data-driven learning?

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UQx Welcome to UQ SLATx 'Improving writing through corpora'! This short online course aims to provide you with the tools, knowledge and skills to become a 'language detective', using special software to improve your academic writing. As a novice writer for your discipline, you may make mistakes with your writing that you don't know how to solve.

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When words sound different in isolation vs. in a sentence, look up the pronunciation first in a dictionary, then use https://youglish.com

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많은 코퍼스가 내장되어 있어서 학생들과 사용하기 좋음.

Graded Reader 텍스트들도 내장되어 있음.

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A complete website for learning about English and French words. You can test your vocabulary level, then work on the words at the level where you are weak. Use wordlists, online concordancer and dictionaries, texts, and a database to store your work and view the work of others. French parallel site is slightly less complete.

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통계, Data visualization 기능이 있음.

튜토리얼 비디오가 있으니 참고할 수 있음.

연구 목적으로 사용 추천.

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#LancsBox: Lancaster University corpus toolbox

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LancsBox is a new-generation software package for the analysis of language data and corpora developed at Lancaster University ■ Leading research in corpus linguistics. ■ 2015 Queen's Aniversary Prize. ■ More info. . New version of #LancsBox, #LancsBox X,is now available with full XML support and the ability to search very large corpora such as the BNC2014.

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Latest Release

A freeware corpus analysis toolkit for concordancing and text analysis.

[ AntConc Homepage] [ Screenshots] [Help] [ License]

Downloads:

Official releases

AntConc 3x series

Older versions

PayPal Donations and Patreon Supporters:

Click one of the following if you want to make a small donation to support the future development of this tool.

Older Versions

All previous releases of AntConc can be found at the following link. <*.exe> and <*_win.zip> are for Windows. <*.dmg>, <*.zip> and <*.pkg> files are for Macintosh OS X. <*.tar.gz> files are for Linux.

Frequent asked questions (FAQ)

1. What is the difference between the Windows-Installer and Windows-Portable versions?
The Windows-Installer version will place the AntConc software in a safe location on your machine and put links to the software in the Start menu and on your desktop (if you want). This is the most common way to get software onto your computer. However, in cases where you are not allowed to install software on the computer (e.g. in a school environment) or when you want to put the software on a portable device like a USB stick, the Windows-Portable version is needed. In this version, all files are stored in a single folder. Open the folder and double click on the AntConc.exe to start. These packaging options were newly introduced with the release of AntConc 4.0.

2. How do I get AntConc to work on a Apple Macintosh computer?
AntConc should work with the latest version of Apple's operating system. Double click the AntConc.dmg icon and follow the instructions to put the software into the Applications folder on your computer. You can then launch the software by double-clicking the icon in the Applications folder or from the Launchpad. This packaging option was newly introduced with the release of AntConc 4

3. Can I use AntConc on a mobile device (e.g. iPhone, Android phone, iPad, or Chromebook)?
No. AntConc can only run on desktop computers.

4. How can I search for a question mark (or other wildcard characters) in my corpus?
The question mark (?) is a wildcard character in AntConc meaning "any one character". To search for an actual question mark or any other wildcard character, just escape the character (e.g. \?) in the search query box. Note that you need to add these characters to the token definition when creating your corpus to be able to search for them later.

5. When creating my own corpus, I get the following error: "Error: UnicodeDecodeError: 'utf-8' codec can't decode byte XXX in position YYY: invalid start byte". What should I do?
This error is telling you that the file is not properly encoded in the default UTF-8 encoding used by AntConc and so it cannot be read. To address this problem, either resave your file using the UTF-8 encoding (recommended) or change the encoding setting of AntConc to match the encoding of the file (assuming you know what encoding your file is saved in). Many users don't know what encoding they are using which is why I recommend that the files are resaved with the UTF-8 encoding. You should be able to set the encoding in any standard text editor, e.g. NotePad or NotePad++.

User Support

Latest Help files

Discussion groups

Video tutorials

YouTube Tutorials (by Laurence Anthony)

YouTube Tutorials (by Umair Ibne Abid of Umair Linguistics)

Youku Tutorials (by Laurence Anthony)

Downloadable guides

Guide by Warren Tang (Hiroshima University, Japan)

Help file translations

Common Statistics Used In Corpus Linguistics

The following document lists common statistics that are used in corpus linguistics focusing on those that appear in AntConc. The document is largely based on an unpublished paper written by Andrew Hardie of Lancaster University in 2014.

Development Road Map

My current tentative plans.

Status

Description

Under development

Plans for 2022
and beyond

Improve tag handling (XML/Part-Of-Speech/...)

Your suggestions come here...

Citing/Referencing AntConc

Use the following method to cite/reference AntConc according to the APA style guide:

Anthony, L. (YEAR OF RELEASE). AntConc (Version VERSION NUMBER) [Computer Software]. Tokyo, Japan: Waseda University. Available from https://www.laurenceanthony.net/software

For example if you download AntConc 4.2.0, which was released in 2022, you would cite/reference it as follows:

Anthony, L. (2022). AntConc (Version 4.2.0) [Computer Software]. Tokyo, Japan: Waseda University. Available from https://www.laurenceanthony.net/software

Word frequency lists

These 'wordlist' corpora can be loaded into AntConc (4.0 and later) via the Corpus Manager and used as reference corpora to create keyword lists.

These lists can be imported into AntConc (3.5.9 and earlier) and used as reference corpora word lists to create keyword lists.

BE06 corpus and AME06 corpus frequency lists.

Brown Corpus frequency lists.

Lemma lists

Lemma lists. These lists can be imported into AntConc (3.5.9 and earlier) to create lemma word lists. From AntConc 4.0, it is recommended that you Part-Of-Speech (POS) tag and lemmatize your files, e.g. using TagAnt, before loading them into AntConc. Then, the lemma forms can be displayed through menu options.

AntBNC Lemma List (ver. 004)
An automatically generated English lemma list based on all words in the BNC corpus with a frequency greater than 2 (created by Laurence Anthony). To use this list, *append* a hyphen (-) character to the AntConc token definition to ensure the processed correctly (see global settings). This list is extensively revised from the previous version, with all words with overlapping headwords combined into a single entry and all words including an apostrophe removed. As this process conflates words of different word classes into the same entry, the list serves more as a flemma list. The automatic generation process naturally introduces some erroneous entries, so the list should be checked before use, depending on the purpose.

Someya Lemma List (no hypens)
An edited version of the Someya English lemma list with no hypenated words (original list created by Yasumasa Someya).

Someya Lemma List

The original Someya English lemma list (created by Yasumasa Someya).

To use this list, *append* a hyphen (-) and apostrophe (') character to the AntConc token definition (see global settings).

French Lemma List
A French lemma list (created by Benoît Sagot).

Spanish Lemma List
A Spanish lemma list (developed by Laurence Anthony based on the list provided here).

Books/papers/websites related to AntConc

References to works citing AntConc can by found on Google Scholar here.

Interesting applications of AntConc

Here are a random collection of projects that use AntConc in interesting ways.

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A freeware corpus analysis toolkit for concordancing and text analysis. [ AntConc Homepage] [ Screenshots] [ Help] [ License] Official releases AntConc 3x series Older versions Click one of the following if you want to make a small donation to support the future development of this tool.

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Find video clips by quote. GetYarn.io now.

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텍스트 분석 및 시각화 도구

설치와 로그인이 필요하지 않음, 텍스트 복사 붙여넣기/파일 업로드/url 입력의 방식으로 이용 가능.

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Parse Tree
Grammatical Structure
Typed Dependencies
Semantic Graph

It provides a simple-to-use graphic interface to the parse tree, grammatical structure, typed dependencies and semantic graph of any text as parsed by the Stanford Parser/Stanford CoreNLP. Written to Java 1.6 and Swing, it is portable across platforms.

It is portable across platforms that support Java 6 and Swing (Linux, Mac Os, Solaris, Windows...)

Run java -jar grammarscope-installer-[version].jar. Shortcuts will be created.

Start (from command line)

Make sure that bin/grammarscope.sh is executable on non-Windows platforms

Run bin/grammarscope.sh (non-Windows) or bin\grammarscope.bat (Windows) from install folder

Alternatively bin/grammarscope-wizard.sh or bin\grammarscope-wizard.bat run from install bin folder, will launch a wizard to create the appropriate scripts. The wizard has a shortcut created when the installer is run.

java -Xmx5G -cp <install-path>/lib/*:<install-path>/stanford/* grammarscope.browser.Browser [parameters]

java -Xmx5G -cp <install-path>\lib\*;<install-path>\stanford\* grammarscope.browser.Browser [parameters]

Note the -Xmx5G parameter sets the JVM memory heap to 5 giga bytes.

The wizard can be run with:

java -jar <install-path>/lib/grammarscope-runnable.jar

The Stanford Parser/CoreNlp engine and models are meant to be in the <install-path>/stanford folder. Every JAR file that this folder contains ends up being included in the classpath. Newer versions of either Parser of CoreNlp can in theory be updated there if they are compatible but they usually require recompiling GrammarScope. So there is no guarantee it will work.

3G will normally do fine when using the Parser. 5G is necessary when using the CoreNLP pipeline.

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Parse TreeGrammatical StructureTyped DependenciesSemantic Graph It provides a simple-to-use graphic interface to the parse tree, grammatical structure, typed dependencies and semantic graph of any text as parsed by the Stanford Parser/Stanford CoreNLP. Written to Java 1.6 and Swing, it is portable across platforms. It is portable across platforms that support Java 6 and Swing (Linux, Mac Os, Solaris, Windows...)

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University of Pennsylvania's Language Data Consortium

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University of Pennsylvania's Language Data Consortium

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Language resources are the collective materials used by those engaged in language-related education, research and technology development. Spanning data collections, corpora, software, research papers and specifications, these vital tools aid and inspire scientific progress.

The Data pages represent the heart of LDC's mission to make language resources available to the broad community. Obtaining Data describes how to license data from LDC's growing online Catalog. LDC members have full access to this database; guest accounts are also available for nonmembers. Students pursuing studies in the field can submit an application to the semi-annual Data Scholarships program.

LDC's Tools pages highlight software developed and distributed by LDC. All tools are available at no cost under an open source license. License details may vary.

The Papers page contains publications by LDC staff about their work and issues of interest in the field and includes papers and slides presented at conferences, journal publications and books.

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Language Resources | Linguistic Data Consortium

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The Linguistic Data Consortium is an international non-profit supporting language-related education, research and technology development by creating and sharing linguistic resources including data, tools and standards.

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Download CoreNLP 4.5.5 CoreNLP on GitHub CoreNLP on 🤗
CoreNLP on Maven
What's new: The v4.5.3 release adds an Ssurgeon interface

About

CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. CoreNLP currently supports 8 languages: Arabic, Chinese, English, French, German, Hungarian, Italian, and Spanish.

Pipeline

The centerpiece of CoreNLP is the pipeline. Pipelines take in raw text, run a series of NLP annotators on the text, and produce a final set of annotations.

CoreDocument

Pipelines produce CoreDocuments, data objects that contain all of the annotation information, accessible with a simple API, and serializable to a Google Protocol Buffer.

Annotations

CoreNLP generates a variety of linguistic annotations, including:

Parts Of Speech

Named Entities

Dependency Parses

Coreference

Quickstart

Download and unzip CoreNLP 4.5.5 (HF Hub)

Download model jars for the language you want to work on and move the jars to the distribution directory. Jars are available directly from us, from Maven, and from Hugging Face.

Thank you to HuggingFace for helping with our hosting!

Include the distribution directory in your CLASSPATH.

You're ready to go! There are many ways to run a CoreNLP pipeline. For instance here's how to run a pipeline on a text file. The output will be available in a file called input.txt.out.

Programming languages and operating systems

Stanford CoreNLP is written in Java; recent releases require Java 8+. You need to have Java installed to run CoreNLP. However, you can interact with CoreNLP via the command-line or its web service; many people use CoreNLP while writing their own code in Javascript, Python, or some other language.

You can use Stanford CoreNLP from the command-line, via its original Java programmatic API, via the object-oriented simple API, via third party APIs for most major modern programming languages, or via a web service. It works on Linux, macOS, and Windows.

License

The full Stanford CoreNLP is licensed under the GNU General Public License v3 or later. More precisely, all the Stanford NLP code is GPL v2+, but CoreNLP uses some Apache-licensed libraries, and so our understanding is that the the composite is correctly licensed as v3+. You can run almost all of CoreNLP under GPL v2; you simply need to omit the time-related libraries, and then you lose the functionality of SUTime. Note that the license is the full GPL, which allows many free uses, but not its use in proprietary software which is distributed to others. For distributors of proprietary software, CoreNLP is also available from Stanford under a commercial licensing You can contact us at java-nlp-support@lists.stanford.edu. If you don't need a commercial license, but would like to support maintenance of these tools, we welcome gift funding: use this form and write "Stanford NLP Group open source software" in the Special Instructions.

Citing Stanford CoreNLP in papers

If you're just running the CoreNLP pipeline, please cite this CoreNLP paper:

Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60. [ pdf] [ bib]

If you're dealing in depth with particular annotators, you're also encouraged to cite the papers that cover individual components: POS tagging, NER, constituency parsing, dependency parsing, coreference resolution, sentiment, or Open IE. You can find more information on the Stanford NLP software pages and/or publications page.

Copyright © 2020 Stanford NLP Group.

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High-performance human language analysis tools, now with native deep learning modules in Python, available in many human languages.

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http://sifnos.sfs.uni-tuebingen.de/VIEW

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VIEW is an ICALL system designed to provide supplementary language learning activities using
authentic texts selected by the learner. VIEW is a multilingual extension of Working with English Real Texts (WERTi),
which has been under development since 2006.