Language variety identification aims at labelling texts in a native lan- guage (e.g. Spanish, Portuguese, English) with its specific variation (e.g. Ar- gentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work we propose a low dimensionality representation (LDR) to address this task with five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain. We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of ∼35%. Furthermore, we compare LDR with two reference distributed representation models. Experimental results show com- petitive performance while dramatically reducing the dimensionality — and in- creasing the big data suitability — to only 6 features per variety. Additionally, we analyse the behaviour of the employed machine learning algorithms and the most discriminating features. Finally, we employ an alternative dataset to test the robustness of our low dimensionality representation with another set of similar languages.
Language variety identification is an author profiling subtask which aims to detect lexical and semantic variations in order to classify different varieties of the same language. In this work we approach the task by using distributed representations based on Mikolov et al. investigations.
Evaluation Dataset and System for Japanese Lexical SimplificationTomoyuki Kajiwara
Tomoyuki Kajiwara, Kazuhide Yamamoto. Evaluation Dataset and System for Japanese Lexical Simplification. In Proceedings of the ACL-IJCNLP 2015 Student Research Workshop, pp.35-40. Beijing, China, July 2015.
Language variety identification aims at labelling texts in a native lan- guage (e.g. Spanish, Portuguese, English) with its specific variation (e.g. Ar- gentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work we propose a low dimensionality representation (LDR) to address this task with five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain. We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of ∼35%. Furthermore, we compare LDR with two reference distributed representation models. Experimental results show com- petitive performance while dramatically reducing the dimensionality — and in- creasing the big data suitability — to only 6 features per variety. Additionally, we analyse the behaviour of the employed machine learning algorithms and the most discriminating features. Finally, we employ an alternative dataset to test the robustness of our low dimensionality representation with another set of similar languages.
Language variety identification is an author profiling subtask which aims to detect lexical and semantic variations in order to classify different varieties of the same language. In this work we approach the task by using distributed representations based on Mikolov et al. investigations.
Evaluation Dataset and System for Japanese Lexical SimplificationTomoyuki Kajiwara
Tomoyuki Kajiwara, Kazuhide Yamamoto. Evaluation Dataset and System for Japanese Lexical Simplification. In Proceedings of the ACL-IJCNLP 2015 Student Research Workshop, pp.35-40. Beijing, China, July 2015.
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Change is inevitable
Defined procedures are required to manage change without preventing change
Software presents many challenges from a control, management, and tracking perspective
Knowing what you have and how you got there is very important
Being able to recreate exactly what is delivered to customers is essential
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This is the first of a series of workshops about information structures, which are the framework for two English writing/speaking textbooks:
Greene & Hunter 2002 "Critical Thinking" (Asahi)
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2. Publication of a research paper in a journal.
3. Acceptance to present at a conference.
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1) the content of the document, and
2) the writing of the document.
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Invisible structures of technical writing
1. The invisible structures
of technical writing:
Pattern recognition for learner executive mode
Lawrie Hunter
http://lawriehunter.com
http://slideshare.net/rolenzo
JACET ESP, Toyo University, December 19, 2015
2. The invisible structures of technical writing:
Pattern recognition for learner executive mode
A set of syllabus design tools
for a non-grammar (pattern recognition) approach to
the development of EFL technical writing skills:
1.Sentence-level information structures
-FRAMEWORK for an entry-level 'critical thinking' syllabus.
-DEMO of application to syllabus/materials design.
2. Information organization structures
-FOR CONTROL of rhetorical flow.
3. A 'processing' heuristic
-FOR CREATING FERTILE VARIATION in language learning activities.
3. No need to write!
Download ev.e.ry.thing
from
lawriehunter.com
or
lawrie's slideshare
slideshare.net/rolenzo
17. Systems view of writing
Grammar
staging
Information
orchestration
Rhetoric, flow
Sentence level
Prescriptive order
charts (linear);
sentence diagrams
Knowledge
structure maps
Topic/stress and
subject-verb
distance
gizmos
Paragraph level Readability charts
Knowledge
structure maps
Old/new and
topic/stress
gizmos
Document level
Readability
outlines
Knowledge
structure maps
Old/new and
topic/stress
gizmos
-don’t you have one?
18. 1. Sentence-level information structures
-FRAMEWORK
for an entry-level 'critical thinking' syllabus.
-DEMO
of application to syllabus/materials design.
19. Structures invariant across languages
Classification Cause-effect Value
Description Sequence Choice
Mohan, B.A. (1986) Language and Content.
Addison and Wesley.
Mohan's 'knowledge structures' Argument
Information organization
Information structures
Syntax structures
Grammar structures
20. Structures invariant across languages
Classification Cause-effect Value
Description Sequence Choice
Mohan's 'knowledge structures' Argument
Information organization
Information structures
Syntax structures
Grammar structures
Comparison Pro-Con
Classification Cause-effect
Description Sequence
Hunter’s ‘information structures'
21. Structures invariant across languages
Argument
Information organization
Information structures
Syntax structures
Grammar structures
22. Do humans have a
GRAPHIC THOUGHT FACILITY?
Hunter: The knowledge structure map is a matrix (confluence)
for the situated learner* and the situated mentor
to confirm context and the nature of "stolen property."**
*Lave, J., & Wenger, E. (1990). Situated Learning: Legitimate Periperal Participation.
Cambridge, UK: Cambridge University Press.
**Brown, J.S. & Duguid, P. (2000) The social life of information. Harvard Business School Press.
<
$$$
!
23. Information structures based curriculum
Critical Thinking
Asahi Press 2001
A writing and presentation
workbook,
6 units (6 genres)
in 30 lessons
Say What You Mean
KUT Press 2006
A writing and mapping
workbook,
5 units (5 genres)
in 30 lessons
Thinking in English
Cengage 2007
A writing and presentation
mapping text/workbook,
5 units (5 genres)
in 30 lessons
24. The learning process used in this book
Each of the 6 units in this book follows the same teaching/learning pattern:
INPUT
USAGE PRACTICE
AUTHENTIC TASK
EXPRESSION
Learning process
- don’t you have one?
Critical Thinking
Asahi Press 2001
A writing and presentation
workbook,
6 units (6 genres)
in 30 lessons
25. Info-structure graphics
embody language features
In terms of language,
this mapping symbolically
constrains link content to
DESCRIPTION CLASSIFICATION COMPARISON SEQUENCE CAUSE-EFFECT Pro-Con
is + ADJ is a kind of is ___er than then so
Clause-whereas-
clause
is used to can be classified as
has more ___
than
after that is a result of
Arg 1 – That’s true
but – Counterarg 1
is made of is one type of
has x% more ___
than
finally leads to
If – Premise 1 (past)
– Arg 1 (would)
etc. etc. etc. etc. etc. etc.
26. Hunter’s ‘information structures’
... reflect information types
(Mohan's (1986) knowledge structures)
rather than speech act types such as SFL's text types.
Classification Principle Evaluation
Description Process Choice
Description
Classification
Comparison
Sequence
Cause-effect
+ inference
Pro-con
32. 1. Sentence-level information structures
2. Information organization structures
Intro
Method
Results
Analysis
Discsussion
Situation
Problem
Solution
Evaluation
Anecdotal lead
Cosmic paragraph
Motley middle
Kicker
Grounds
Modality
CLAIM
Warrant
Backing
General
Specific
33. 1. Sentence-level information structures
2. Information organization structures
Intro
Method
Results
Analysis
Discsussion
Situation
Problem
Solution
Evaluation
Anecdotal lead
Cosmic paragraph
Motley middle
Kicker
Grounds
Modality
CLAIM
Warrant
Backing
General
Specific
Some other time!
Some other time!
34. 3. A 'processing' heuristic
-FOR CREATING FERTILE VARIATION
in language learning activities.
36. Thank you
for your kind attention.
Don’t hesitate to write to me.
I share!
Lawrie Hunter
lawriehunter.com
lawriehunter@gmail.com
37. BioLawrie Hunter is currently working in the Center for Professional Communication at GRIPS in
Roppongi.
He was a member of the founding team of Kochi University of Technology, and before that
he created the intercultural communication program for Kochi University. He has worked as
a mathematics teacher and counsellor in Canadian high schools and as a mathematics
teacher trainer in Papua New Guinea.
His main research themes are document design for education; computer assisted language
learning; information structures as a framework for EFL curricula; non-grammar approaches
to academic writing; and concept/argument mapping for low text representation of complex
ideas.
Textbook publications: Critical Thinking (Asahi); Thinking in English (Cengage); How
Academic Writing Works (Minaminokaze).
Lawrie is now active in the Tokyo poetry scene.