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Here is the link to English corpora that are available online.

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corpora corpus english american iweb movies tv bnc byu coca coha time soap glowbe word frequency

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English Corpora: most widely used online corpora. Billions of words of data: free online access

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Compare genres, dialects, time periods. Search by PoS, collocates, synonyms, and much more.

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If you know/like different languages, here is a link to Spanish corpora.

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corpora corpus spanish español word frequency

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Corpus del Español: 10 billion words: Dialects / Genres / Historical

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Largest full-featured corpora of Spanish: Search by PoS, collocates, synonyms, genre, dialect, historical, etc. Downloadable data also.

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And one more.

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El Corpus del Español del Siglo XXI (CORPES XXI) es, al igual que CREA, un corpus de referencia. En lingüística, se llama corpus a un conjunto formado por miles de textos (novelas, obras de teatro, guiones de cine, noticias de prensa, ensayos, transcripciones de noticiarios radiofónicos o televisivo…

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A linguistic corpus is a collection of texts where one can find the typical use of a single word or a phrase, as well as their meaning and grammatical function. Nowadays, without access to a language corpus, it has become impossible to do linguistic research, to write dictionaries, grammars and language teaching books, to create search engines sensitive to Polish inflection, machine translation engines and software of advanced language technology. Language corpora have become an essential tool for linguists, but they are also helpful for software engineers, scholars of literature and culture, historians, librarians and other specialists of art and computer sciences.

There already exist national corpora compiled by the British, Germans, Czech and Russians. Polish people also need an extensive, well balanced language corpus - a language source which can be accessed online.

The National Corpus of Polish is a shared initiative of four institutions: Institute of Computer Science at the Polish Academy of Sciences (coordinator), Institute of Polish Language at the Polish Academy of Sciences, Polish Scientific Publishers PWN, and the Department of Computational and Corpus Linguistics at the University of Łódź. It has been carried out as a research-development project of the Ministry of Science and Higher Education.

These four institutions have started cooperation to build a reference corpus of Polish language containing over fifteen hundred millions of words. The corpus is searchable by means of advanced tools that analyse Polish inflection and the Polish sentence structure.

The list of sources for the corpora contains classic literature, daily newspapers, specialist periodicals and journals, transcripts of conversations, and a variety of short-lived and internet texts. For a corpus to be reliable, not only it is necessary to contain a high number of words, but it also needs a diversity of texts with respect to the subject and genre. The conversations ought to represent both male and female speakers, in various age groups, coming from various regions in Poland.

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NKJP - The National Corpus of Polish - the biggest, linguistically annotated corpus of contemporary polish language

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Home

Welcome to AntLab! Please check out my recent and upcoming events, and if you see me at a conference please remember to say hello. Also, my software tools are getting quite a lot of attention recently. So, please make sure you visit the software page to get the latest versions of AntConc, AntWordProfiler, and my other tools. On Twitter, you can find me at @antlabjp.

Popular works

© 2024 Laurence Anthony. All rights reserved.

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The website of Laurence Anthony, Professor, Waseda University Japan.

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As our first exercise, download and start AntConc now, and familiarise yourself with the elements of the program window a little. Also have a quick browse through the menus to get a first impression of the options, even though you may not understand what they all mean yet.

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Once you have explored the interface, let us open the tab "File" and click on "Open Corpus Manager...". Try to download the corpus "AmE06". Tick the box "Connect Online" and then click the button "Update". Once the corpus has been downloaded, click the button "Return to Main Window".

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14.10.24 Find lexemes. Make observations.

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14.10.24 Find lexemes. Make observations.

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Once you are in the main window, click on the tab "KWIC". Now, please look for these lexemes: (1) society, (2) technology and (3) freedom.

Investigate the context:

Type each word one by one into the KWIC search bar.

How many times do these words occur in the corpus?

Browse through the concordance lines (the snippets of text showing how each word is used in context).

Observe the words that appear to the left and right of each word. These words give you insight into the common collocations (words that tend to appear together).

As you explore the context of each word:

Pay attention to any patterns (e.g. formal/informal context, frequent phrases).

Note if the words that appear on the left and/or right form some common categories (i.e. lexical fields).

What are the most common lexical fields?

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A semantic field is a set of words (or lexemes) related in meaning. The phrase is also known as a word field, lexical field, field of meaning, and semantic system. Linguist Adrienne Lehrer has defined semantic field more specifically as "a set of lexemes which cover a certain conceptual domain and which bear certain specifiable relations to one another" (1985).

Examples and Observations

The subject matter often unites a semantic field.

"The words in a semantic field share a common semantic property. Most often, fields are defined by subject matter, such as body parts, landforms, diseases, colors, foods, or kinship relations....

"Let's consider some examples of semantic fields....The field of 'stages of life' is arranged sequentially, though there is considerable overlap between terms (e.g., child, toddler) as well as some apparent gaps (e.g., there are no simple terms for the different stages of adulthood). Note that a term such as minor or juvenile belongs to a technical register, a term such as kid or tot to a colloquial register, and a term such as sexagenarian or octogenarian to a more formal register. The semantic field of 'water' could be divided into a number of subfields; in addition, there would appear to be a great deal of overlap between terms such as sound/fjord or cove/harbor/bay."

(Laurel J. Brinton, "The Structure of Modern English: A Linguistic Introduction." John Benjamins, 2000)

Metaphors and Semantic Fields

Semantic fields are also sometimes called fields of meaning:

"Cultural attitudes to particular areas of human activity can often be seen in the choices of metaphor used when that activity is discussed. A useful linguistic concept to be aware of here is that of semantic field, sometimes called just field, or field of meaning....

"The semantic field of war and battle is one that sports writers often draw on. Sport, particularly football, in our culture is also associated with conflict and violence."

(Ronald Carter, "Working With Texts: A Core Introduction to Language Analysis." Routledge, 2001)

More and Less Marked Members of a Semantic Field

Color terms also help illustrate how words are grouped into a semantic field.

"In a semantic field, not all lexical items necessarily have the same status. Consider the following sets, which together form the semantic field of color terms (of course, there are other terms in the same field):

1. Blue, red, yellow, green, black, purple

2. Indigo, saffron, royal blue, aquamarine, bisque

The colors referred to by the words of set 1 are more 'usual' than those described in set 2. They are said to be less marked members of the semantic field than those of set 2. The less marked members of a semantic field are usually easier to learn and remember than more marked members. Children learn the term blue before they learn the terms indigo,, royal blue, or aquamarine. Often, a less marked word consists of only one morpheme, in contrast to more marked words (contrast blue with royal blue or aquamarine). The less marked member of a semantic field cannot be described by using the name of another member of the same field, whereas more marked members can be thus described (indigo is a kind of blue, but blue is not a kind of indigo).

"Less marked terms also tend to be used more frequently than more marked terms; for example, blue occurs considerably more frequently in conversation and writing than indigo or aquamarine....Less marked terms are also often broader in meaning than more marked terms.... Finally, less marked words are not the result of the metaphorical usage of the name of another object or concept, whereas more marked words often are; for example, saffron is the color of a spice that lent its name to the color."

(Edward Finegan. "Language: Its Structure and Use, 5th ed." Thomson Wadsworth, 2008)

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A semantic field is a set of words (or lexemes) related in meaning. The phrase is also known as a word field, lexical field, field of meaning, and semantic system. Linguist Adrienne Lehrer has defined semantic field more specifically as "a set of lexemes which cover a certain conceptual domain and which bear certain specifiable relations to one another" (1985).

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light illumination

emotion feeling

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please add comments

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false and untrue:

false - (65 total hits), patterns: combination of words: "true or false",

untrue - (2 total hits) - not enough examples to detect any pattern

always and forever

always - (487 hits) patterns: modal auxiliary + always

forever - (54 total hits) - used in two meanings:

as "zawsze" and "na zawsze"

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car vehicle

love hate

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car (200 hits) vehicle (83 hits)

love (347 hits) hate (26 hits)

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LOYALTY VS DEVOTION

LOYALTY - 6 times

Loyalty to

Loyalty and commitment.

Sign of loyalty

DEVOTION - 16 times

Devotion to something

Devotion and admiration

Religious devotion - used in religious context

LUCK VS PROSPERITY

PROSPERITY- 19 times

Prosperity of something

Prosperity for

Level of prosperity

LUCK- 116 times

Bad luck

Good luck

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person (238) human (439)

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cute (13)

cute as...

adorable (4)

all (2615)

all the way

all the more

all the time

every (570)

every time

every day

every one

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Advanced - 40 times

Connected to TECHNOLOGY, STAGE, STATE, AGE

Proper names:

Bangladesh Centre for Advanced Studies

Institute for Advanced Studies

Advanced Mixed Waste Treatment Project

Primitive - 15 times

Used to portray the connection to nature for example "Primitive Nature", "Primitive Tribe", "Primitive Races"

Both words can be used with word "Capitalism"

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smart (39) clever (12)

smart and sth

smart enough to; for

smart women, therapist

clever

clever strategy, navigations, research

clever enough to

person (238) human (439)

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Light: 263 times all together

noun

with preposition of

Light of the World (christian tradition)

Adjectives(white, bright, tiny, pink) verb - shed

Illumination: no hits found

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In today's meeting, let us explore wordforms. From morphological perspective we can sigle out: (1) inflectional morphemes and (2) derivational morphemes.

First, let us consider the inflectional forms of these verbs: say, talk, report. AntConc enables its users to look for various wordforms at once by using the wildcard (*).

In this activity, please select three different subcorpora in which you can find these verbs. Use: (1) AmE06_A_press_reportage, (2) AmE_06_P_romance and (3) AmE_06_R_humor.

In your searches, please use concordance lines (KWIC) and the wildcard (*). Answer these questions:

(1) Which wordforms can be observed in each subcorpora?

(2) Are there are dominant wordforms in each subcorpora?

(3) Are there any characteristic grammatical patters for each verb in the three corpora?

In this activity, please use simple statistics. Note the observed frequencies for each wordform in the three corpora. Write down any characteristic, reoccuring grammatical patterns.

_____

Now, let us explore derivational forms of some lexemes. Consider these words: dream, expect, hope. Have you identified any derivational forms of these words in the three corpora? If so, please answer these questions:

(1) What derivational forms can you observe in the subcorpora?

(2) Are they used with their standard, dictionary meanings? (for reference, please use the Merriam-Webster dictionary)

(3) Which of these lexemes yields the greatest number of derivational forms?

In this activity, please use simple statistics. Note the observed frequencies for each wordform in the three corpora. Write down any characteristic, reoccuring grammatical patterns.

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18.11.2024 t-score and MI-score for collocations (2-grams)

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18.11.2024 t-score and MI-score for collocations (2-grams)

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18.11.2024 Basic statistical measures and comparing corpora

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18.11.2024 Basic statistical measures and comparing corpora

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In today's class, we will learn how to use the function "Cluster" in AntConc. To exemplify, how this works, we will search the occurrences of modal verbs in selected sub-corpora of "British English 2006".

Before identifying and analysing the occurrences of modal verbs. Let us clarify a few aspects.

According to Kennedy (2002: 73), "verbs constitute about 20 per cent of all the word tokens used in English, and in written texts, modal verbs typically constitute about 8 per cent of all verb forms. Modals form a small, semi-closed set of nine auxiliary verbs, most of which express both core ‘deontic’ meanings such as ‘obligation’, ‘intention’ or ‘permission’ (e.g. ‘You must be home by 10 pm’) or ‘epistemic’ meanings associated with truth conditions and assessment of degrees of certainty (e.g. ‘You must be our new neighbours’). The ‘central’ English modals are usually considered to be will, would, can, could, may, might, shall, should, must."

For this reason, in our analyses, we will use only the nine basic modal verbs. We will exclude semi-modals.

Notice, that so far, some research regarding modal verbs has been conducted. Scholars have investigated: (1) uses of modals in different varieties of English (e.g. Australian, British and American English) (Collins 1991) or (2) the distribution of the modals should and must from the 17th century to the present in different genres (Biber et al. 1998).

----TASK

[1] Go to AntConc and in the Corpus Manager explore the subcorpora of the British Corpus.

[2] Select the registers that you find the most interesting. Read through the descriptions. Pay particular attention to the Summary row.

[3] In your study, you will have to identify:

(a) the distribution of the nine modal verbs across the selected corpora,

(b) identify the syntactic patterns in modal verb phrases,

(c) note the observed frequency and calculate the normalized frequency for modal verbs and modal verb phrases,

(d) visualize your findings,

(d) what conclusions can you draw on the basis of the collected data?

You may work in groups. Please download the report sheet and prepare your answers. Use Excel to do any calculations.

Once you are done with this analysis, you may want to extend your search by looking into contexts in which modal verbs occur. Are they always used in their prototypical situations/contexts? Consider this paper: https://journals.openedition.org/anglophonia/2453

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Meaning in Context and Contextual Meaning: A Perspective on the Sem...

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Everybody agrees that the context has a vital role to play in communication; observing that the meaning of an utterance is determined in context is stating the obvious. The downside of this commo…

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Dear all!

I have created a Google spreadsheet where you can assemble your teams and assign tasks to team members.

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Once you have collected your data, you need to label it correctly. Notice! While using Excel (or other similar software/tools), keep your data in separate "places" (i.e. columns, rows, sheets). In other words, try to organize your data in a clear way so as to observe dependencies between variables and to group your findings.

Do not place numerical data and labels together in one cell. Keep it separately. If you investigate and compare more than one data set (e.g. corpus), try to use individual sheets.

Try to use colours to mark similar data and to distinguish it from different sets.

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More: https://www.researchgate.net/profile/Stefan-Gries-2/publication/267242111_Useful_statistics_for_corpus_linguistics/links/5466341a0cf2f5eb180168f4/Useful-statistics-for-corpus-linguistics.pdf

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from: Brezina, V. (2018). Statistics in Corpus Linguistics. Cambridge: Cambridge University Press.

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from: Brezina, V. (2018). Statistics in Corpus Linguistics. Cambridge: Cambridge University Press.

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from: Brezina, V. (2018). Statistics in Corpus Linguistics. Cambridge: Cambridge University Press.

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from: Brezina, V. (2018). Statistics in Corpus Linguistics. Cambridge: Cambridge University Press.

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from: Brezina, V. (2018). Statistics in Corpus Linguistics. Cambridge: Cambridge University Press.

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from: Brezina, V. (2018). Statistics in Corpus Linguistics. Cambridge: Cambridge University Press.

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09.12.2024 Building a corpus / Reference lists / Annotation

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09.12.2024 Building a corpus / Reference lists / Annotation