LINGUIST List 28.1397

Tue Mar 21 2017

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

Editor for this issue: Yue Chen <yuelinguistlist.org>


Date: 20-Mar-2017
From: Fabian Tomaschek <fabian.tomaschekuni-tuebingen.de>
Subject: Introduction to the Naive Discriminative Learning Package/Germany
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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.tomaschekuni-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.tomaschekuni-tuebingen.de

Apply by Email: fabian.tomaschekuni-tuebingen.de

Registration Instructions:
Registration via personal email

Page Updated: 21-Mar-2017