28.1397, Summer Schools: Introduction to the Naive Discriminative Learning Package/Germany

Tue Mar 21 17:48:36 UTC 2017

LINGUIST List: Vol-28-1397. Tue Mar 21 2017. ISSN: 1069 - 4875.

Subject: 28.1397, Summer Schools: Introduction to the Naive Discriminative Learning Package/Germany

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Date: Tue, 21 Mar 2017 13:48:19
From: Fabian Tomaschek [fabian.tomaschek at uni-tuebingen.de]
Subject: Introduction to the Naive Discriminative Learning Package/Germany

Introduction to the Naive Discriminative Learning Package/Germany

Host Institution: Universität Tübingen
Coordinating Institution: Universität zu Köln
Website: http://pape2017.uni-koeln.de/introduction-to-the-naive-discriminative-learning-package/

Dates: 14-Jun-2017 - 14-Jun-2017
Location: Cologne, Germany

Focus: Im giving a workshop on "Naive Discriminative Learning" (NDL) at the PaPE conference in June. NDL might be interesting for all linguists who investigate, for example, perception and production of synonyms and homonyms, of idioms, of phonetic variation, morphological mapping and processing.

NDL enables you to create computational learning models and test their predictions. NDL has proven to be predictive for effects in simple naming tasks, priming tasks and lexical decision tasks.
Minimum Education Level: MA

Description:
Organisers:

Fabian Tomaschek (University of Tübingen, fabian.tomaschek at uni-tuebingen.de)

Workshop time and place:

The workshop will be held in Cologne on June 14, 2017, as part of the
Phonetics and Phonology in Europe (PaPE) conference.

For whom is this workshop:

Due to its implementation, NDL is capable to learn many-to-one as well as
one-to-many relations. This enables all researches who investigate, for
example, perception and production of synonyms and homonyms, of idioms, of
phonetic variation, morphological mapping and processing.
NDL enables you  to create computational learning models and test their
predictions. NDL has proven to be predictive for effects in simple naming
tasks, priming tasks and lexical decision tasks.

Computational models offer today’s linguists a possibility to formalize their
theoretical assumptions and draw precise predictions about the speaker’s and
listener’s linguistic behavior in experiments such as lexical response times
or identification rates and in corpora such as phonetic durations or formant
values. Based on learning algorithms tested repeatedly in animal learning
behavior  (Rescorla & Wagner, 1972),  the Naive Discriminative Learning
Package for R (NDL) represents such a possibility.

Workshop description:

After having introduced the theoretical background on NDL, the workshop will
allow participants to construct event-based data frames in order to model
cue-to-outcome association in a hands-on manner. Furthermore, techniques will
be shown how to predict linguistic behavior such as response times, phonetic
durations or lexical categorization.

How NDL works:

NDL calculates association strengths between an input layer and an output
layer, the former representing cues and the latter outcomes of e.g. perceptual
processes. Crucially, like speakers, NDL is sensitive to the statistical
properties of words (e.g. Aylett & Turk, 2004; Jurafsky et al., 2000).
Different learning environments as well as different levels of speech
production or perception can be constructed by different combinations of cues
and outcomes. I.e. input units can be represented among others by letters or
phones, output units can contain word forms, lexemes or grammatical
categories. This allows us to create different models of linguistic
processing.

Modeling learning can be based on two different input sources: Either
individual events such as corpora of transcribed spoken language, e.g.
Buckeye, Kiel, etc.; or corpora of linguistic forms containing counts, e.g.
CELEX. So far, NDL has been used to predict response times in lexical decision
tasks, neural behavior, phonetic productions, dialectal distances etc. (e.g.
Baayen et. al., 2011; Wieling et al., 2014; Augurzky et al., 2014).

Requirements and Participants:

The number of participants is limited to 30. Submission is based on a “first
come first attend” basis. Since this is not a programming course, participants
should have programming abilities as well as knowledge of statistical modeling
in R (knowledge of dataframes, accessing and indexing variables, usage of
for-loops, etc.). Participants interested in an introduction to R are referred
to my introduction to R (http://fabian-tomaschek.com/useful-stuff/).
Participants are encouraged to prepare their own experimental and corpus data.

Linguistic Field(s): Computational Linguistics
                      Text/Corpus Linguistics

Registration: 01-May-2017 to 01-Jun-2017

Contact Person: Fabian Tomaschek
                Email: fabian.tomaschek at uni-tuebingen.de

Apply by Email: fabian.tomaschek at uni-tuebingen.de

Registration Instructions:
Registration via personal email

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