The 15thInternational American Association for Corpus Linguistics Conference (AACL2020) will take place September 18-19 2020 at Northern Arizona University
Main conference general program Call for papers. We invite contributions on a broad and inclusive basis. There are three categories of proposals (full papers, posters and panels). All proposals will be peer-reviewed by the conference program committee. The conference will feature three thematic streams in the general program. The thematic streams are as follows:
Linguistic analyses of corpora as they relate to language use (e.g., register/genre variation, lexical and grammatical variation, language varieties, historical change, lexicography)
Application (the use of corpora in language teaching and learning, as well as other applied fields such as testing and legal research)
Tools and methods (corpus creation, corpus annotation, tagging and parsing, corpus analysis software)
Submission categories
There will be three categories of presentations at the conference: Full papers Consisting of a 20-minute talk followed by 5 minutes for questions and discussion. Submissions should present completed research where substantial results have been achieved. (Work in progress should be submitted as a poster abstract.) Abstracts should be 300 words (maximum), excluding the word count for references. Posters Posters can present either results of completed research or work in progress.. We especially welcome poster abstracts that (a) report on innovative research that is in its early phases, or (b) report on new software or corpus data resources. Abstracts should be 200 words (maximum), excluding the word count for references. Panels Panels during the main conference offer an opportunity to group related papers together to allow for extended discussions. Proposals for panels should include the abstracts for the individual presentations (300 words max), together with an introductory abstract (200 words max) introducing the overall goals of the panel. Panels will be allocated time slots up to a maximum of 2 hours.
Pre-conference Workshops Half-day pre-conference workshops will take place on Thursday Sept 17. Abstracts for submission (max. 300 words) should include a complete description of the half-day workshop (max time 3 hours).
Submission guidelines: Submit abstracts to aacl@nau.edu by January 30, 2020. Cover page: Author(s) name(s); Affiliation; Contact information; Title; Submission Category and thematic stream
Abstract page: Submission category; Title; Abstract Format: MS Word or PDF (the latter is necessary if the abstract contains specialized fonts).
NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.
The second aspect is summarised in that phrase “potential patterns”. How so? The process operates in two stages. First, all the effort of a concordancer or a word-listing application goes into reducing a vast and complex object to a much simpler shape. That is, a set of 100 million words on a confusing wealth of topics in a variety of styles and produced by innumerable people for a lot of different reasons gets reduced to a mere list in alphabetical order. A rich chaos of language is reduced, it is “boiled down” to a simpler set. In the vapours that have steamed off are all the facts about who wrote the texts and what they meant.We have therefore lost a great deal in that process, and if it damaged the original texts we would never dare do it.
The advantage comes in the second stage where one examines the boiled down extract, the list of words, the concordance. It is here that something not far different from the sometimes-scorned “intuition” comes in. This is imagination. Insight. Human beings are unable to see shapes, lists, displays, or sets without insight, without seeing in them “patterns”. It seems to be a characteristic of the homo sapiens mind that it is often unable to see things “as they are” but imposes on them a tendency, a trend, a pattern. From the earliest times, the very stars in the sky have been perceived as belonging in “constellations”. This capability can come at a cost, of course: it may be easy to spot a pattern in a cloud or in a constellation and thereby build up a mistaken theory; but the point is that it is this ordinary imaginative capacity, that of seeing a pattern, which is there in all of us and which makes it possible for corpus-based methods to make a relatively large impact on language theory. For with these twin resources, namely the tools to manipulate a lot of data in many different ways and without wasting much time, combined with the power of imagination and pattern-recognition, it becomes possible to chase up patterns that seem to be there and come up with insights affecting linguistic theory itself. The tools we use generate patterns (lists, plots, colour arrangements) and it is when we see these that in some cases the pattern “jumps out” at us. In other cases we may need training to see the patterns but the endeavour is itself largely a search for pattern.
Scott, M. & Tribble, C. 2006. Textual Patterns: keyword and corpus analysis in language education. Amsterdam: Benjamins. (pp. 5-6).
Online editor that can help you to improve word choice and writing style, and, optionally, help you to detect grammar mistakes and plagiarism. To check word count, simply place your cursor into the text box above and start typing. You’ll see the number of characters and words increase or decrease as you type, delete, and edit them. You can also copy and paste text from another program over into the online editor above. The Auto-Save feature will make sure you won’t lose any changes while editing, even if you leave the site and come back later.
i Case-insensitive
m Multiline : allow the grep engine to match at ^ and $ after and before at \r or \n.
s Magic Dot : allows . to match \r and \n
x Free-spacing: ignore unescaped white space; allow inline comments in grep patterns.
(?imsx) On
(?-imsx) Off
(?i-msx) Mixed
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Regex Meta-Characters:
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. Any character except newline or carriage return
[ ] Any single character of set
[^ ] Any single character NOT of set
0 or more previous regular expression
*? 0 or more previous regular expression (non-greedy)
1 or more previous regular expression
+? 1 or more previous regular expression (non-greedy)
? 0 or 1 previous regular expression
| Alternation
( ) Grouping regular expressions
^ Beginning of a line or string
$ End of a line or string
{m,n} At least m but most n previous regular expression
{m,n}? At least m but most n previous regular expression (non-greedy)
\1-9 Nth previous captured group
\& Whole match # BBEdit: ‘&’ only – no escape needed
` Pre-match # PCRE? NOT BBEdit
\’ Post-match # PCRE? NOT BBEdit
+ Highest group matched # PCRE? NOT BBEdit
\A Beginning of a string
\b Backspace(0x08)(inside[]only) # PCRE?
\b Word boundary(outside[]only)
\B Non-word boundary
\d Digit, same as[0-9]
\D Non-digit
\G Assert position at end of previous match or start of string for first match
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Case-Change Operators
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\E Change case – acts as an end delimiter to terminate runs of \L & \U.
\l Change case of only the first character to the right lower case. (Note: lowercase ‘L’)
\L Change case of all text to the right to lowercase.
\u Change case of only the first character to the right to uppercase.
\U Change case of all text to the right to uppercase.
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White-Space or Non-White-Space
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\t Tab
\n Linefeed
\r Return
\R Return or Linefeed or Windows CRLF (matches any Unicode newline sequence).
\f Formfeed
\s Whitespace character equivalent to [ \t\n\r\f]
\S Non-whitespace character
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\W Non-word character
\w Word character[0-9A-Za-z_]
\z End of a string
\Z End of a string, or before newline at the end
(?#) Comment
(?:) Grouping without backreferences
(?=) Zero-width positive look-ahead assertion
(?!) Zero-width negative look-ahead assertion
(?>) Nested anchored sub-regexp stops backtracking
(?imx-imx) Turns on/off imx options for rest of regexp
(?imx-imx:…) Turns on/off imx options, localized in group # ‘…’ indicates added regex pattern
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PERL-STYLE PATTERN EXTENSIONS : BBEdit Documentation : ‘…’ indicates added regex pattern
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Extension Meaning
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(?:…) Cluster-only parentheses, no capturing
(?#…) Comment, discard all text between the parentheses
(?imsx-imsx) Enable/disable pattern modifiers
(?imsx-imsx:…) Cluster-only parens with modifiers
(?=…) Positive lookahead assertion
(?!…) Negative lookahead assertion
(?<=…) Positive lookbehind assertion
(?…) Match non-backtracking subpattern (“once-only”)
(?R) Recursive pattern
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POSITIONAL ASSERTIONS (duplicatation of above)
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POSITIVE LOOKBEHIND ASSERTION: (?<=’pattern’) # Lookbehind Assertions are of fixed-length
NEGATIVE LOOKBEHIND ASSERTION: (?<!’pattern’)
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SPECIAL CHARACTER CLASSES (POSIX standard except where ‘Perl Extension’ is indicated):
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CLASS MEANING
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[[:alnum:]] Alpha-numeric characters
[[:alpha:]] Alphabetic characters
[[:ascii:]] Character codes 0-127 # Perl Extension
[[:blank:]] Horizontal whitespace
[[:cntrl:]] Control characters
[[:digit:]] Decimal digits (same as \d)
[[:graph:]] Printing characters, excluding spaces
[[:lower:]] Lower case letters
[[:print:]] Printing characters, including spaces
[[:punct:]] Punctuation characters
[[:space:]] White space (same as \s)
[[:upper:]] Upper case letters
[[:word:]] Word characters (same as \w) # Perl Extension
[[:xdigit:]] Hexadecimal digits
Usage example of multiple character classes:
[[:alpha:][:digit:]]
«Negated» character class example:
[[:^digit:]]+
** POSIX-style character class names are case-sensitive
** The outermost brackets above indicate a RANGE; the class name itself looks like this: [:alnum:]
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CONDITIONAL SUBPATTERNS
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Conditional subpatterns allow you to apply “if-then” or “if-then-else” logic to pattern matching.
The “if” portion can either be an integer between 1 and 99, or an assertion.
The forms of syntax for an ordinary conditional subpattern are:
If the condition evaluates as true, the “yes-pattern” portion attempts to match. Otherwise, the
“no-pattern” portion does (if there is a “no-pattern”).
Source: Melissa Bond, Olaf Zawacki-Richter & Mark Nichols. 2019. Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology. British Journal of Educational Technology, 50,1, 12–63.
