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Newcastle University Eprints
Newcastle University ePrints Knight D, Adolphs S, Carter R. CANELC: constructing an e-language corpus. Corpora 2014, 9(1), 29-56. Copyright: The definitive version of this article, published by Edinburgh University Press, 2014, is available at: http://dx.doi.org/10.3366/cor.2014.0050 Always use the definitive version when citing. Further information on publisher website: www.euppublishing.com Date deposited: 23-07-2014 Version of file: Author Accepted Manuscript This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License ePrints – Newcastle University ePrints http://eprint.ncl.ac.uk CANELC – Constructing an e-language corpus ___________________________________________________________________________ Dawn Knight1, Svenja Adolphs2 and Ronald Carter2 This paper reports on the construction of CANELC: the Cambridge and Nottingham e- language Corpus 3 . CANELC is a one million word corpus of digital communication in English, taken from online discussion boards, blogs, tweets, emails and SMS messages. The paper outlines the approaches used when planning the corpus: obtaining consent; collecting the data and compiling the corpus database. This is followed by a detailed analysis of some of the patterns of language used in the corpus. The analysis includes a discussion of the key words and phrases used as well as the common themes and semantic associations connected with the data. These discussions form the basis of an investigation of how e-language operates in both similar and different ways to spoken and written records of communication (as evidenced by the BNC - British National Corpus). Keywords: Blogs, Tweets, SMS, Discussion Boards, e-language, Corpus Linguistics 1. Introduction Communication in the digital age is a complex many faceted process involving the production and reception of linguistic stimuli across a multitude of platforms and media types (see Boyd and Heer, 2006:1). -
RASLAN 2015 Recent Advances in Slavonic Natural Language Processing
RASLAN 2015 Recent Advances in Slavonic Natural Language Processing A. Horák, P. Rychlý, A. Rambousek (Eds.) RASLAN 2015 Recent Advances in Slavonic Natural Language Processing Ninth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2015 Karlova Studánka, Czech Republic, December 4–6, 2015 Proceedings Tribun EU 2015 Proceedings Editors Aleš Horák Faculty of Informatics, Masaryk University Department of Information Technologies Botanická 68a CZ-602 00 Brno, Czech Republic Email: [email protected] Pavel Rychlý Faculty of Informatics, Masaryk University Department of Information Technologies Botanická 68a CZ-602 00 Brno, Czech Republic Email: [email protected] Adam Rambousek Faculty of Informatics, Masaryk University Department of Information Technologies Botanická 68a CZ-602 00 Brno, Czech Republic Email: [email protected] This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the Czech Copyright Law, in its current version, and permission for use must always be obtained from Tribun EU. Violations are liable for prosecution under the Czech Copyright Law. Editors ○c Aleš Horák, 2015; Pavel Rychlý, 2015; Adam Rambousek, 2015 Typography ○c Adam Rambousek, 2015 Cover ○c Petr Sojka, 2010 This edition ○c Tribun EU, Brno, 2015 ISBN 978-80-263-0974-1 ISSN 2336-4289 Preface This volume contains the Proceedings of the Ninth Workshop on Recent Advances in Slavonic Natural Language Processing (RASLAN 2015) held on December 4th–6th 2015 in Karlova Studánka, Sporthotel Kurzovní, Jeseníky, Czech Republic. -
Children Online: a Survey of Child Language and CMC Corpora
Children Online: a survey of child language and CMC corpora Alistair Baron, Paul Rayson, Phil Greenwood, James Walkerdine and Awais Rashid Lancaster University Contact author: Paul Rayson Email: [email protected] The collection of representative corpus samples of both child language and online (CMC) language varieties is crucial for linguistic research that is motivated by applications to the protection of children online. In this paper, we present an extensive survey of corpora available for these two areas. Although a significant amount of research has been undertaken both on child language and on CMC language varieties, a much smaller number of datasets are made available as corpora. Especially lacking are corpora which match requirements for verifiable age and gender metadata, although some include self-reported information, which may be unreliable. Our survey highlights the lack of corpus data available for the intersecting area of child language in CMC environments. This lack of available corpus data is a significant drawback for those wishing to undertake replicable studies of child language and online language varieties. Keywords: child language, CMC, survey, corpus linguistics Introduction This survey is part of a wider investigation into child language and computer-mediated communication (CMC)1 corpora. Its aim is to assess the availability of relevant corpora which can be used to build representative samples of the language of children online. The Isis project2, of which the survey is a part, carries out research in the area of online child protection. Corpora of child and CMC language serve two main purposes in our research. First, they enable us to build age- and gender-based standard profiles of such language for comparison purposes. -
Email in the Australian National Corpus
Email in the Australian National Corpus Andrew Lampert CSIRO ICT Centre, North Ryde, Australia and Macquarie University, Australia 1. Introduction Email is not only a distinctive and important text type but one that touches the lives of most Australians. In 2008, 79.4% of Australians used the internet, ahead of all Asia Pacific countries except New Zealand (Organisation for Economic Co-operation and Development, 2008). A recent Nielsen study suggests that almost 98% of Australian internet users have sent or received email messages in the past 4 weeks (Australian Communications and Media Authority, 2008), making email the most used application on the internet by a significant margin. It seems logical to embrace a communication medium used by the vast majority of Australians when considering the text types and genres that should be included in the Australian National Corpus. Existing corpora such as the British National Corpus (2007) and the American National Corpus (Macleod, Ide, & Grishman, 2000) provide many insights and lessons for the creation and curation of the Australian National Corpus. Like many existing corpora, the British National Corpus and the American National Corpus contain language data drawn from a wide variety of text types and genres, including telephone dialogue, novels, letters, transcribed face-to-face dialogue, technical books, newspapers, web logs, travel guides, magazines, reports, journals, and web data. Notably absent from this list are email messages. In many respects, email has replaced more traditional forms of communication such as letters and memoranda, yet this reality is not reflected in existing corpora. This lack of email text is a significant gap in existing corpus resources. -
C 2014 Gourab Kundu DOMAIN ADAPTATION with MINIMAL TRAINING
c 2014 Gourab Kundu DOMAIN ADAPTATION WITH MINIMAL TRAINING BY GOURAB KUNDU DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2014 Urbana, Illinois Doctoral Committee: Professor Dan Roth, Chair Professor Chengxiang Zhai Assistant Professor Julia Hockenmaier Associate Professor Hal DaumeIII, University of Maryland Abstract Machine learning models trained on labeled data of a domain degrade performance severely when tested on a different domain. Traditional approaches deal with this problem by training a new model for every new domain. In Natural language processing, top performing systems often use multiple interconnected models and therefore training all of them for every new domain is computationally expensive. This thesis is a study on how to adapt to a new domain, using the system trained on a different domain, avoiding the cost of retraining. This thesis identifies two key ingredients for adaptation without training: broad coverage re- sources and constraints. We show how resources like Wikipedia, VerbNet, WordNet that contain comprehensive coverage of entities, semantic roles and words in English can help a model adapt to a new domain. For the task of semantic role labeling, we show that in the decision phase, we can replace a linguistic unit (e.g. verb, word) with another equivalent linguistic unit residing in the same cluster defined in these resources (e.g. VerbNet, WordNet) such that after replacement, text becomes more like text on which the model was trained. We show that the model's output is more accurate on the transformed text than on original text. -
CS224N Section 3 Corpora, Etc. Pi-Chuan Chang, Friday, April 25
CS224N Section 3 Corpora, etc. Pi-Chuan Chang, Friday, April 25, 2008 Some materials borrowed from Bill’s notes in 2006: http://www.stanford.edu/class/cs224n/handouts/cs224n-section3-corpora.txt Proposal for Final project due two weeks later (Wednesday, 5/7) Look for interesting topics? Æ go through the syllabus : http://www.stanford.edu/class/cs224n/syllabus.html Æ final projects from previous years : http://nlp.stanford.edu/courses/cs224n/ Æ what data / tools are out there? Æ collect your own dataset 1. LDC (Linguistic Data Consortium) o http://www.ldc.upenn.edu/Catalog/ 2. Corpora@Stanford o http://www.stanford.edu/dept/linguistics/corpora/ o Corpus TA: Anubha Kothari o The inventory: http://www.stanford.edu/dept/linguistics/corpora/inventory.html o Some of them are on AFS; some of them are available on DVD/CDs in the linguistic department 3. http://nlp.stanford.edu/links/statnlp.html 4. ACL Anthology o http://aclweb.org/anthology-new/ 5. Various Shared Tasks o CoNLL (Conference on Computational Natural Language Learning) 2006: Multi-lingual Dependency Parsing 2005, 2004: Semantic Role Labeling 2003, 2002: Language-Independent Named Entity Recognition 2001: Clause Identification 2000: Chunking 1999: NP bracketing o Machine Translation shared tasks: 2008, 2007, 2006, 2005 o Pascal Challenges (RTE, etc) o TREC (IR) o Senseval (Word sense disambiguation) o … Parsing Most widely-used : Penn Treebank 1. English: (LDC99T42) Treebank-3 (see Bill’s notes) 2. In many different languages, like Chinese (CTB6.0), Arabic Other parsed corpora 1. Switchboard (spoken) 2. German: NEGRA, TIGER, Tueba-D/Z • There’s an ACL workshop on German Parsing this year… 3. -
Machine Learning Approaches To
Slides adapted from Dan Jurafsky, Jim Martin and Chris Manning ` This week x Finish semantics x Begin machine learning for NLP x Review for midterm ` Midterm ◦ October 27th, ◦ Where: 1024 Mudd (here) ◦ When: Class time, 2:40-4:00 ◦ Will cover everything through semantics ◦ A sample midterm will be posted ◦ Includes multiple choice, short answer, problem solving ` October 29th ◦ Bob Coyne and Words Eye: Not to be missed! ` TBD: Class outing to Where the Wild Things Are ` A subset of WordNet sense representation commonly used ` WordNet provides many relations that capture meaning ` To do WSD, need a training corpus tagged with senses ` Naïve Bayes approach to learning the correct sense ◦ Probability of a specific sense given a set of features ◦ Collocational features ◦ Bag of words ` A case statement…. ` Restrict the lists to rules that test a single feature (1-decisionlist rules) ` Evaluate each possible test and rank them based on how well they work. ` Glue the top-N tests together and call that your decision list. ` On a binary (homonymy) distinction used the following metric to rank the tests P(Sense1 | Feature) P(Sense2 | Feature) ` This gives about 95% on this test… ` In vivo versus in vitro evaluation ` In vitro evaluation is most common now ◦ Exact match accuracy x % of words tagged identically with manual sense tags ◦ Usually evaluate using held-out data from same labeled corpus x Problems? x Why do we do it anyhow? ` Baselines ◦ Most frequent sense ◦ The Lesk algorithm ` Wordnet senses are ordered in frequency order ` So “most -
Corpus Studies in Applied Linguistics
106 Pietilä, P. & O-P. Salo (eds.) 1999. Multiple Languages – Multiple Perspectives. AFinLA Yearbook 1999. Publications de l’Association Finlandaise de Linguistique Appliquée 57. pp. 105–134. CORPUS STUDIES IN APPLIED LINGUISTICS Kay Wikberg, University of Oslo Stig Johansson, University of Oslo Anna-Brita Stenström, University of Bergen Tuija Virtanen, University of Växjö Three samples of corpora and corpus-based research of great interest to applied linguistics are presented in this paper. The first is the Bergen Corpus of London Teenage Language, a project which has already resulted in a number of investigations of how young Londoners use their language. It has also given rise to a related Nordic project, UNO, and to the project EVA, which aims at developing material for assessing the English proficiency of pupils in the compulsory school system in Norway. The second corpus is the English-Norwegian Parallel Corpus (Oslo), which has provided data for both contrastive studies and the study of translationese. Altogether it consists of about 2.6 million words and now also includes translations of English texts into German, Dutch and Portuguese. The third corpus, the International Corpus of Learner English, is a collection of advanced EFL essays written by learners representing 15 different mother tongues. By comparing linguistic features in the various subcorpora it is possible to find out about non-nativeness generally and about problems shared by students representing different languages. 1 INTRODUCTION 1.1 Corpus studies and descriptive linguistics Corpus-based language research has now been with us for more than 20 years. The number of recent books dealing with corpus studies (cf. -
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CHAPTER II REVIEW OF RELATED LITERATURE In this chapter, the researcher presents the result of reviewing related literature which covers Corpus based analysis, children short stories, verbs, and the previous studies. A. Corpus Based Analysis in Children Short Stories In these last five decades the work that takes the concept of using corpus has been increased. Corpus, the plural forms are certainly called as corpora, refers to the collection of text, written or spoken, which is systematically gathered. A corpus can also be defined as a broad, principled set of naturally occurring examples of electronically stored languages (Bennet, 2010, p. 2). For such studies, corpus typically refers to a set of authentic machine-readable text that has been selected to describe or represent a condition or variety of a language (Grigaliuniene, 2013, p. 9). Likewise, Lindquist (2009) also believed that corpus is related to electronic. He claimed that corpus is a collection of texts stored on some kind of digital medium to be used by linguists for research purposes or by lexicographers in the production of dictionaries (Lindquist, 2009, p. 3). Nowadays, the word 'corpus' is almost often associated with the term 'electronic corpus,' which is a collection of texts stored on some kind of digital medium to be used by linguists for research purposes or by lexicographers for dictionaries. McCarthy (2004) also described corpus as a collection of written or spoken texts, typically stored in a computer database. We may infer from the above argument that computer has a crucial function in corpus (McCarthy, 2004, p. 1). In this regard, computers and software programs have allowed researchers to fairly quickly and cheaply capture, store and handle vast quantities of data. -
The Best Kept Secrets with Corpus Linguistics
The Best Kept Secrets with Corpus Linguistics Neil Cooke,1 Lee Gillam2 and Ahmet Kondoz1 Abstract This paper presents the use of corpus linguistics techniques on supposedly “clean” corpora and identifies potential pitfalls. Our work relates to the task of filtering sensitive content, in which data security is strategically important for the protection of government and military information, and of growing importance in combating identity fraud. A naïve keyword filtering approach produces a large proportion of false positives, and the need for more fine-grained approaches, suggests the consideration of using corpus linguistics for such content filtering. We present work undertaken on the Enron corpus, a collection of emails that has had various tasks undertaken on various portions and versions of it by other researchers. We have made some efforts to reconcile differences between the different versions by considering what impact some of these versions have on our results. Our anticipated efforts in using automatic ontology learning [Gillam, Tariq and Ahmad 2005; Gillam and Ahmad 2005], and local grammars [Ahmad, Gillam and Cheng 2006] to discover points of interest within the Enron corpus have been re- oriented to the problem of discovering “confidentiality banners” with a view to removing them from the collection. Our work to date makes strong use of collocation patterns [Smada 1993] to identify the signatures of these banners, and to use the banners themselves to better understand the different versions of the Enron corpus. We further consider the use of extended collocation patterns to identify text “zones”, following [Teufel and Moens 2000], and the subsequent potential for sentiment analysis [Klimt and Yang 2004]; [Pang, Lee and Vaithyanathan 2002] 1 Introduction To obtain ever-more robust statistical results, scientists look towards ever-larger datasets. -
Distributed Memory Bound Word Counting for Large Corpora
Democratic and Popular Republic of Algeria Ministry of Higher Education and Scientific Research Ahmed Draia University - Adrar Faculty of Science and Technology Department of Mathematics and Computer Science A Thesis Presented to Fulfil the Master’s Degree in Computer Science Option: Intelligent Systems. Title: Distributed Memory Bound Word Counting For Large Corpora Prepared by: Bekraoui Mohamed Lamine & Sennoussi Fayssal Taqiy Eddine Supervised by: Mr. Mediani Mohammed In front of President : CHOUGOEUR Djilali Examiner : OMARI Mohammed Examiner : BENATIALLAH Djelloul Academic Year 2017/2018 Abstract: Statistical Natural Language Processing (NLP) has seen tremendous success over the recent years and its applications can be met in a wide range of areas. NLP tasks make the core of very popular services such as Google translation, recommendation systems of big commercial companies such Amazon, and even in the voice recognizers of the mobile world. Nowadays, most of the NLP applications are data-based. Language data is used to estimate statistical models, which are then used in making predictions about new data which was probably never seen. In its simplest form, computing any statistical model will rely on the fundamental task of counting the small units constituting the data. With the expansion of the Internet and its intrusion in all aspects of human life, the textual corpora became available in very large amounts. This high availability is very advantageous performance-wise, as it enlarges the coverage and makes the model more robust both to noise and unseen examples. On the other hand, training systems on large data quantities raises a new challenge to the hardware resources, as it is very likely that the model will not fit into main memory. -
The Enron Corpus: Where the Email Bodies Are Buried?
The Enron Corpus: Where the Email Bodies are Buried? Dr. David Noever Sr. Technical Fellow, PeopleTec, Inc. www.peopletec.com 4901-D Corporate Drive Huntsville, AL 35805 USA [email protected] Abstract To probe the largest public-domain email database for indicators of fraud, we apply machine learning and accomplish four investigative tasks. First, we identify persons of interest (POI), using financial records and email, and report a peak accuracy of 95.7%. Secondly, we find any publicly exposed personally identifiable information (PII) and discover 50,000 previously unreported instances. Thirdly, we automatically flag legally responsive emails as scored by human experts in the California electricity blackout lawsuit, and find a peak 99% accuracy. Finally, we track three years of primary topics and sentiment across over 10,000 unique people before, during and after the onset of the corporate crisis. Where possible, we compare accuracy against execution times for 51 algorithms and report human-interpretable business rules that can scale to vast datasets. Introduction The 2002 Enron fraud case uncovered financial deception in the world’s largest energy trading company and at the time, triggered the largest US bankruptcy and its most massive audit failure [1]. For the previous six years, Fortune magazine had named Enron “America’s most innovative company.” By 1999, Enron was brokering a quarter of all electricity and natural gas deals [1]. Piggybacking on the internet bubble, Enron devised methods to sell everything and own nothing. Problematically, the company could assign its own revenue (mark-to-market) and then bury its losses using off-book debt in shell companies (or Special Purpose Entities called Raptor and Chewco).