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Corpus Linguistics

Corpus Linguistics

Corpus linguistics is a methodology in linguistics that involves computer-based empirical analyses (both quantitative and qualitative) of actual patterns of language use by employing electronically available, large collections of naturally occuring spoken and written texts, so-called corpora. Corpus-based and other types of empirical linguistic research have shown that speakers' intuitions oftentimes provide only limited access to the open-ended nature of language, which can cause problems when examining unexpected or infrequent linguistic structures, e.g. as regards lexical co-occurrence patterns, patterns of variation between grammatical constructions, word meaning, or idioms and metaphorical language.

The factors that condition the choice between competing grammatical variants is one topic that features prominently in research and students' projects at Mainz University. While grammar books make us believe that e.g. yet is a trigger of present perfect, we can observe U.S. election campaigns featuring the sentence "Did you vote yet?". While standard reference works used by school teachers advise pupils to use the synthetic comparative -er with monosyllabic adjectives, we observe native speakers to use more apt. more proud rather than prouder. apter in the majority of cases. While the 's -genitive is described as being used with persons while the of -genitive is allegedly to be used with things, linguists who do research on actual language use find a marked discrepancy between what is taught and what is done. Thus, the topic's relevance cannot be stigmatized as an exception or even be marked as incorrect. The issue of variation poses an intriguing challenge for English teachers and researchers. While to some the task of bringing schoolbook knowledge up to scratch with actual language use seems insurmountable, English Linguistics at Mainz University tries to offer ways out of the dilemma.

Most (advanced) English linguistics classes in Mainz involve at some point students' own collection, processing and analysis of empirical data, often by making use of electronic corpora. In advanced classes in particular, students will be asked to carry out corpus-based projects, sometimes involving replications and extensions of earlier case studies. The Department of English and Linguistics hence offers its students a wide range of computerized corpora comprising British and American English. MACOCO. the Mainz Corpus Collection, is a progressively enhanced source for student research on the correctness, use, historical development, etc. of certain language structures.

Examples for research projects with electronic corpora as research tools
  • Investigating near-synonymous words (sick vs. ill )
  • Word-forming elements and how their use can be related to changes in society and culture, historical events or fashion (-dom as in kingdom. -nik as in peacenik. -thon as in sleepathon. -gate as in nipplegate )
  • Changes (of preferences) in language use such as the rise and fall of words and phrases, or changes within grammatical constructions (help + to infinitive as in I helped him to carry the boxes vs. help + bare infinitive as in I helped him _ carry the boxes )
Applications of corpus-based research
  • Foreign language teaching: Materials and syllabus design, language testing, and classroom methodology
  • Corpus information is extensively used in lexicography: Almost all monolingual learner dictionaries are now corpus-based, e.g. the Longman Dictionary of Contemporary English
  • Corpus-based reference and student grammars of English:
    • Biber, Douglas et al. (1999) Longman Grammar of Spoken and Written English. London: Longman.
    • Biber, Douglas et al. (2002) Longman Student Grammar of Spoken and Written English. London: Longman.
    • Huddleston, Rodney and Geoffrey K. Pullum (2005) A Student's Introduction to English Grammar. Cambridge: CUP.
    • Huddleston, Rodney and Geoffrey K. Pullum, eds. (2002) The Cambridge Grammar of the English Language. Cambridge: CUP.
» Selected readings: Corpus Linguistics
  • Biber, Douglas et al. (1998) Corpus Linguistics: Investigating Language Structure and Use. Cambridge: CUP.
  • Hoffmann, Sebastian et al. (2008) Corpus Linguistics with BNCweb - a Practical Guide. Frankfurt/Main: Peter Lang.
  • Lemnitzer, Lothar & Zinsmeister, Heike (2006) Korpuslinguistik. Eine Einführung. Tübingen: Narr.
  • McEnery, Tony & Wilson, Andrew (²2001) Corpus Linguistics. Edinburgh: Edinburgh University Press.
  • McEnery, Tony, Yukio Tono & Xiao, Richard (2006) Corpus-based Language Studies: An Advanced Resource Book. London: Routledge.
  • Mukherjee, Joybrato (2009) Anglistische Korpuslinguistik. Eine Einführung. Berlin: Erich Schmidt.
  • Partington, Alan (2001) "Corpora and their use in language research". In: Aston, Guy (ed.), Learning with Corpora. Bologna: CLUEB: 46-62.
  • Scherer, Carmen (2006) Korpuslinguistik. Eine Einführung. Heidelberg: Winter.
» Selected links (external) Zusatzinformationen zu dieser Seite
  • Name der Seite: English Linguistics
  • 29.10.2013

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Essay on Corpus - 1673 Words

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3.1 Collection of text: The raw text for the corpus was collected from the prothom-alo home page- WWW.Prothom-alo.net. This was done using a web crawler program that surfed through the website of prothom-alo and downloaded all the news available for the year of 2005- including magazines, periodicals published by them. The crawler crawled for one night to collect all the text, which were in html of course. After that using the Linux script all the files were converted to text files.

decemb er day1 day12 day 31

News categories 1,2. 15,38

Fig: Prothom-alo corpus 3.2 Unicode convertioin: The second part in creating the corpus was to convert all the files to unicode format. This was needed because Unicode support for Bangla is much more rich than any other format. Prothom-alo uses two types of fonts, namely “Bansi Alpona” and “Prothoma”. The previous was in use up to 2005 and currently they are using “Prothoma” for the on lone version of the newspaper. So a Java application was written which recursively searched the folders and sub folders and convert all the text files to Unicode.

4. Processing the text: Categorizing the news: After converting to Unicode the corpus is now ready for any further processing required. An important and useful processing could be categorizing the news. Prothom-alo presents news in 27 different categories. Each category has a category id; i.e. category 1 is for “prothom pata”, category 2 is for “sesh pata”, etc. So if all the news that belong to the same category can be merged together it will enable us to analyze and carry out some research on like text categorization etc. A Java application is used which surfs through the news of all the days and collects news of the same category in one file. The corpus is also available as a single text file. 5. Analysis. We now have a corpus which is: • •

318 Mb in size 12 million word/token count

which is a big one. Some basic statistical analysis is now on offer which.

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 British National Corpus Table of Contents Introduction 2 Introduction to the British National Corpus 2 What is a corpus . 3 What is a corpus for? 5 Earlier interest in computerised corpora for linguistic research 6 Corpus Design and Construction 7 Written language, spoken language 7 A monolingual corpus 8 Conclusion 9 Introduction The paper aims to analyse the introduction to the British national Corpus program that is a lingual program of leaning more than 100 million expressions and words that a learner learn as a monolingual group. Moreover, it discusses the program from different angles to understand its concept. Lastly, it focuses on reasons and considerations that is taken under notice while selecting a non-UK based language course for studying. Introduction to the British National Corpus The British National Corpus is an accumulation of 100 million words of contemporary British English content held in PC intelligible structure. It is accessible as an examination device for those professionally keen on how the English language is being utilised as a part of the late twentieth century inside the United Kingdom. These incorporate especially word specialists and makers of English language reference lives up to expectations, additionally scholarly etymologists. The hunt/recovery programming supplied with the corpus will.

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Corpus linguistics - an introduction - Englisches Seminar

Corpus linguistics - an introduction What is a corpus?

A corpus (plural corpora. German “das Korpus”, not “der”) is a collection of texts used for linguistic analyses, usually stored in an electronic database so that the data can be accessed easily by means of a computer. Corpus texts usually consist of thousands or millions of words and are not made up of the linguist’s or a native speaker’s invented examples but on authentic (naturally occurring) spoken and written language.

The majority of present-day corpora are “balanced” or “systematic”. This means that the texts are collected (“compiled”) according to specific principles, such as different genres, registers or styles of English (e.g. written or spoken English, newspaper editorials or technical writing); these sampling principles do not follow language-internal but language-external criteria. For example, the texts for a corpus are not selected because of their high number of relative clauses but because they are instances of a predefined text type, say broadcast English in a hypothetical corpus of Australian British English. Examples of balanced corpora are the International Corpus of English (ICE). the British National Corpus (BNC). or the Brown and Lancaster-Oslo/Bergen (LOB) corpora and their Freiburg updates (Frown and F-LOB ).

What is corpus linguistics and why is it useful?

Based on the above definition of a corpus, corpus linguistics is the study of language by means of naturally occurring language samples; analyses are usually carried out with specialised software programmes on a computer. Corpus linguistics is thus a method to obtain and analyse data quantitatively and qualitatively rather than a theory of language or even a separate branch of linguistics on a par with e.g. sociolinguistics or applied linguistics. The corpus-linguistic approach can be used to describe language features and to test hypotheses formulated in various linguistic frameworks. To name but a few examples, corpora recording different stages of learner language (beginners, intermediate, and advanced learners) can provide information for foreign language acquisition research; by means of historical corpora it is possible to track the development of specific features in the history of English like the emergence of the modal verbs gonna and wanna ; or sociolinguistic markers of specific age groups such as the use of like as a discourse marker can be investigated for purposes of sociolinguistic or discourse-analytical research.

The great advantage of the corpus-linguistic method is that language researchers do not have to rely on their own or other native speakers’ intuition or even on made-up examples. Rather, they can draw on a large amount of authentic, naturally occurring language data produced by a variety of speakers or writers in order to confirm or refute their own hypotheses about specific language features on the basis of an empirical foundation.

What types of corpora are there?

In the following, a list of some of the most common types of corpora is provided.

  • General corpora, such as the British National Corpus or the Bank of English. contain a large variety of both written and spoken language, as well as different text types, by speakers of different ages, from different regions and from different social classes.
  • Synchronic corpora, such as F-LOB and Frown. record language data collected for one specific point in time, e.g. written British and American English of the early 1990s.
  • Historical corpora, such as ARCHER and the Helsinki corpus, consist of corpus texts from earlier periods of time. They usually span several decades or centuries, thus providing diachronic coverage of earlier stages of language.
  • Learner corpora, such as the International Corpus of Learner English and the Cambridge Learner Corpus. are collections of data produced by foreign language learners, such as essays or written exams.
  • Corpora for the study of varieties, such as the International Corpus of English and the Freiburg English Dialect Corpus. represent different regional varieties of a language.

There is also a large variety of specialized corpora, e.g. Michigan Corpus of Academic Spoken English (MICASE ), useful for various types of research (cf. e.g. http://www.helsinki.fi/varieng/CoRD/corpora/index.html ).

It should be pointed out that the above listed types of corpora are not necessarily mutually exclusive – F-LOB and Frown. for example, are both synchronic and regional corpora, and even “become” historical when paired with their 1960s counterparts LOB and Brown .

2. List of corpora available in Freiburg

You can download a list of corpora here. Please note that with new corpora constantly being compiled this list is not exhaustive but constitutes a selection of well known and widely used corpora of the English language.

3. Software

In order to analyse a corpus and search for certain words or phrases (strings), you need special software. Some software packages are designed for a specific corpus, for example Sara for the BNC or ICECUP for the ICE Great Britain. ‘Concordancers’, on the other hand, can be used for the analysis of almost any corpus.

One of the most frequently used concordancers is Wordsmith Tools. Its two most important tools, Concord and WordList. will be explained in more detail below.

As an alternative to Wordsmith, you can also use a concordancer called AntConc which can be downloaded for free. At www.antlab.sci.waseda.ac.jp/antconc_index.html you will find both links for the download and an online help system explaining its basic functions. The most useful functions of AntConc are explained below.

3.1 WordSmith Concord

Click on the Wordsmith icon on the desktop to open the program. Select concord in order to search a corpus for a certain word or phrase. You can now choose a corpus and select those files of the corpus you want to analyse.

As a case study, let us analyse the use of English prepositional phrases by German and Italian learners of English in the International Corpus of Learner English (ICLE). The underlying assumption is that German learners frequently use ‘possiblity to do something’ (native language interference from German ‘Möglichkeit, etwas zu tun’) while Italian learners prefer an of -construction as the direct translation of possibilità di fare qc (possibility of doing sth.).

In the ‘choose text ’ option, mark all texts written by Italian learners (those beginning with ‘it’) and put them into the directory by drag and drop. Click ok.

Then go to concord – settings – search word. e.g. ‘possibility’. and click go now (see below for further options to type in a search word or phrase). This will get all occurrences of ‘possibility’ for you to analyse. For a better overview, you can sort them e.g. according to the first word to the right.

The number on the left indicates the number of occurrences; on the right, further information, such as the source files, is provided. In the toolbar, you find a number of functions which are useful to work with the data.

In order to view the samples with larger or smaller context, click on view - grow or view - shrink .

You can also resort (edit - resort) data according to words on the first to fifth item to the left or the right of ‘possibility’. To compare the use of prepositions occurring with ‘possibility’, for example, sort according to first word right (R1).

Additionally, you can delete irrelevant examples by pressing delete on your keyboard and then choosing the zap -option ( edit - zap ) from the toolbar. In our example, all hits where ‘possibility’ is not used with a verb phrase (e.g. ‘the possibility for women’, ‘possibility of an adoption’) are deleted, leaving only the occurrences of ‘possibility’ + prepositional verb phrase.

Furthermore, you can view the most frequent collocations or the most frequent clusters by clicking on the respective tabs below. This will show you that Italian learners use ‘possibility to’ (39 occurrences) slightly less often than possibility of’ (43 occurrences), thus neither firmly corroborating nor contradicting the above hypothesis:

Now you can start a new concordance, e.g. with the German ICLE subcorpus and compare the results.

To continue working with this data at a later point in time, you can save it either as a concord file ( file - save as ). If you do not have Wordsmith on your computer at home, it is better to save the data as a .txt file or as an excel spreadsheet as you will not be able to open a Concord file without the Wordsmith software. Saving it as an excel table allows you to work with the data later on, e.g. to copy and paste some of the examples, or to count the occurrences and compare frequencies of different corpora/ points in time and finally draw graphics.

Some further options for entering a search word or phrase:

By using the asterisk *, you can widen the scope of your search. For example, entering going as a search word will provide you only with all instances of going ; entering going to with all instances of going to. If you type in go*. on the other hand, you will get all words beginning with go -, e.g. going, goes, gold. Searching for *ing. you will get all words ending in –ing. e.g. swimming, dancing, sing .

You can also type in several words as your search words, e.g. go / going / goes.

Additionally, it is possible to analyse the co-occurrence of two words within a certain distance. In order to do this, you need to type in one word as ‘Search Word or Phrase’ and the other as ‘Context Words’ in the tab ‘advanced’. Additionally, indicate the ‘search horizons’, i.e. how many words to the left or right the second word might occur with respect to the first. For example, if you want to analyse a collocation such as have a look in a span of five words, enter have/ has/ had having as search word and alook as context word; click on ‘0L’ and ‘5R’ to find all instances where look is found within five words right of book.

WordSmith WordList

The tool WordList generates word lists of the selected text files and enables you to compare the length of text files or corpora. Moreover, you can use WordList to compare the frequency of a word in different text files or across genres and to identify common clusters.

Choose the text files for your analysis as described in the section above and use WordList now instead of Concord.

In the tabs below you can select between three different types of word lists being listed by their frequency. occurring in alphabetical order or containing statistical information.

‘Wordlist statistics’ compares the frequencies of words in each category of the respective corpus (e.g. in each text of the German and Italian sub-corpora of the ICLE), providing information on the number of words, the average length of words and sentences, etc.

For further information and explanations of the different tools, you can always resort to the Wordsmith Tools Help window.

3.2 AntConc Concordance tool

This tool shows the words or word strings you want to analyse in their textual context.

2. Select the files you want to analyse: File > Open file(s)

3. Choose the tab "Concordance"

4. Type in a search word (“Search Term”, bottom left-hand corner)

Example: how to find all occurrences of make :

- only one word form: type in make, makes, made, making separately

- several word forms: use of wildcards

i. ma* gives you all of the above word forms, but also all other words beginning in ma-. e.g. man, mankind, marry. etc. * stands for 0 or more characters

ii. ma?e gives you make and made. but also maze, male and mate. stands for any 1 character

i. @ stands for 0 or 1 word

ii. # stands for any one word

iii. | stands for OR

2) Determine how large the context of the concordance line is supposed to be: Default setting of “Search Window Size” is 50 characters, but generally you need more context à 200 or 250 characters

3) Click “Start”

4) “Concordance Hits” shows you the overall amount of occurrences (remember that not all occurrences need to be relevant for your analysis!)

5) If you want to see the whole text of one concordance line, move the mouse over the highlighted search term in the concordance line and click.

6) Deleting unwanted concordance lines: on your keyboard press “control” + click on the line you want to delete, then press “delete” on your keyboard. Click on “Sort” (under the “Search Term” box to reorder the remaining concordance lines so that you are left with consistent numbering.

7) Save your results: File > Save output to text file

How to refine your search:

- Click on “Advanced” next to the “Search Term” box

- Type in make in the “Search Term” box

- Activate the box “Contexts Words and Horizons”

- Type in “up” in the box under “Context Words”, then click on “Add”

- Define the search horizon (e.g. 0 words to the right and 5 words to the left of make )

- Click on “Apply”

- Click on “Start”

Example 2: Finding words clustering around take

- Select the tab “Clusters”

- Type in take as search term

- “Search Term Position”: Decide if you want to find the words preceding (activate “on right”, i.e. take is on the right) or following take (activate “on left”, i.e. take is on the left)

- Using “Cluster Size”, define how long you want your cluster to be (e.g. at least 3 words including take )

- Click on “Start”

Example 3: Finding collocates of take

- Select the tab “Collocates”

- Type in take as search term

- Define the span of words to the left and right of take. “Window span” from e.g. 0L to 5R

- Click on “Start”


- When you have done a search with context words via the “Advanced” search function, and then want to do a search without context words, make sure to clear the context words you used for your previous search.

- When you are using a part-of-speech-tagged corpus like F-LOB or Frown, and you do not want the tags to show up, go to “Global Settings” > “Tag Settings” > “Hide Tags”

4. Exercises

1. The BNC online (www.natcorp.ox.ac.uk ) offers a free search facility for simple searches. For example, you can check whether a certain collocation is used by British native speakers. The BNC online counts all instances of your search items but displays at most 50 random examples.

a) Here you can find some translations of German collocations. Do they exist in English and are they used by native British speakers?

  • arm wie eine Kirchenmaus = poor as a church mouse
  • die Ansprüche runterschrauben = to screw down one’s standards
  • ausflippen = to flip out
  • den Nagel auf den Kopf treffen = to hit the nail on the head
  • das Geld zum Fenster rausschmeißen = to throw money out of the window
  • den Wald vor lauter Bäumen nicht mehr sehen = to not see the wood for the trees

b) The following words have a different meaning in German and English. Check their use in the BNC and verify their meaning in the OED (www.oed.com ; can only be accessed in the campus network):

2. If you want to analyse different varieties of English, you can use the ICE (International Corpus of English) corpora. For this exercise, refer to the ICE New Zealand.

a) ‘Wahine’ is a word from Maori meaning woman/female/wife. How many occurrences can you find both in the written and in the spoken part of ICE NZ?

b) The word ‘panache’ occurs once in ICE NZ - in which file?

c) Find the word nice in ICE NZ. Which adverb does it most frequently collocate with?

d) Which is the most efficient search strategy for finding all instances of to shake x’s head (including its inflected forms)?

5. Points to consider when conducting corpus-linguistic research
  • Make sure you have enough time to conduct your corpus-linguistic research! Don’t start two or three days before your actual presentation – you should be finished by then! Depending on your topic/research question, you’ll need two or three weeks to analyse your features.
  • Choose your corpus/corpora carefully: a large corpus is usually suitable for any kind of linguistic research (1,000,000 words or more), while a small corpus (200,000 to 500,000 words) may only be sufficient for frequent syntactic structures such as the present perfect or the analysis of the more common modal verbs.
  • Get to know your corpus/corpora: text types, size, language variety, etc.
  • If you compare two or more different corpora, e.g. the German and Swedish ICLE sub-corpora, be aware that each sub-corpus may consist of a different number of words (e.g. 265,341 words in the German ICLE. 248,578 words in the Swedish ICLE ). When you present your corpus-linguistic results, you have to make sure that your figures are comparable. It is no use saying that feature X occurred 5 times in the German ICLE and 5 times in the Swedish ICLE. if the total numbers of words differ in the two corpora – you have to have a common basis. You can solve this problem by extrapolating your figures to a common denominator. A frequently used common denominator in corpus-linguistic research is 1 million words, but you can also use other figures, e.g. 250,000 words. This is how you calculate the extrapolation: Multiply the feature you counted in corpus A by 1,000,000, then divide this figure by the actual size of corpus A. E.g. feature X occurred 5 times in 265,341 words in the German ICLE à 5 multiplied by 1,000,000 = 5,000,000 divided by 265,341 = 18.84 occurrences of feature X in 1 million words. You then do the same calculation with the results from the Swedish ICLE. 5 multiplied by 1,000,000 divided by 258,978 = 20.11 occurrences of feature X in 1 million words. Although the differences between the two corpora used here are only minimal you still have to do the extrapolation. Otherwise you would be comparing apples and oranges!
  • Be careful to find all occurrences of your feature! If, for example, you search for the collocation make a decision, your search strategy has to be such that you find all inflectional variants of MAKE (mak* would give you make, makes, making but not made. However, it also gives you maker. Ma* would give you also made but then you are faced with any word starting in ma-. such as man, mankind, mad, Mary. etc.) Also, DECISION might be pre-modified by an adjective such as useful or personal which you might want to include in your analysis as well, so make sure you don’t forget these examples during your search (e.g. by using the search string a * decision ).
  • Not all concordance lines need to be relevant for your research. If you search for the phrasal verb make up, you will find a number of nominal or adjectival uses of this phrasal verb, such as “She put on her make up” or “Her beautifully made up face”. In WordSmith, you can discard such unwanted concordance lines by highlighting them, then pressing delete. When you have marked all unwanted examples in your concordance in this way, you use the “zap” function so that the unwanted examples are discarded and you are left only with those occurrences you actually need.
  • A high frequency of your researched feature does not necessarily mean that your feature is distributed evenly across the entire corpus you used. Check the corpus’ file names in order to exclude that maybe only one or two authors or speakers produced all the examples you have found.
  • Make sure you don’t over-generalise your results. If, for example, you used a very small corpus of written academic American English, you mustn’t claim that your results are valid for American English as a whole or even for English in general. Qualify your research results by saying that your results hold only as far as written academic American English is concerned and that further research into other types of English needs to be conducted for more general conclusions about the features you researched.
6. Further reading
  • Baker, Paul, Andrew Hardie & Tony McEnery. 2006. A Glossary of Corpus Linguistics. Edinburgh: Edinburgh University Press.
  • Biber, Douglas, Conrad, Susan, & Reppen, Randi. 1998. Corpus Linguistics: Investigating Language Structure and Use. Cambridge: CUP.
  • Fillmore, Charles J. 1992. ““Corpus linguistics” or “Computer-aided armchair linguistics””. In: Svartvik, Jan (ed.) Directions in Corpus Linguistics. Berlin: de Gruyter. 35-60.
  • Kennedy, Graeme. 1998. An introduction to Corpus Linguistics. London & New York: Longman.
  • Leech, Geoffrey. 1992. “Corpora and theories of linguistic performance”. In: Svartvik, Jan (ed.) Directions in Corpus Linguistics. Berlin: de Gruyter. 105-122.
  • McEnery, Tony & Andrew Wilson. 2001. Corpus Linguistics. Edinburgh: Edinburgh UP.
  • Meyer, Charles F. 2002. English Corpus Linguistics: An Introduction. Cambridge: CUP.
  • Scherer, Carmen. 2006. Korpuslinguistik. Heidelberg: Winter.
  • Teubert, Wolfgang & Anna Cermáková. 2007. Corpus linguistics. A short introduction. London: Continuum.
  • www.helsinki.fi/varieng/CoRD/corpora/index.html
  • www1.ids-mannheim.de/fileadmin/lexik/lehre/engelberg/Webseite. /Skript_02.pdf