Extracting aspects of determiner meaning from dialogue in a virtual world environment

We use data from a virtual world game for automated learning of words and grammatical constructions and their meanings. The language data are an integral part of the social interaction in the game and consist of chat dialogue, which is only constrained by the cultural context, as set by the nature of the provided virtual environment. Building on previous work, where we extracted a vocabulary for concrete objects in the game by making use of the non-linguistic context, we now target NP/DP grammar, in particular determiners. We assume that we have captured the meanings of a set of determiners if we can predict which determiner will be used in a particular context. To this end we train a classifier that predicts the choice of a determiner on the basis of features from the linguistic and non-linguistic context.

[1]  Deb Roy,et al.  Probabilistic grounding of situated speech using plan recognition and reference resolution , 2005, ICMI '05.

[2]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL 2006.

[3]  Raymond J. Mooney,et al.  Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language , 2014, J. Artif. Intell. Res..

[4]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[5]  Johanna D. Moore,et al.  Report on the First NLG Challenge on Generating Instructions in Virtual Environments (GIVE) , 2009, ENLG.

[6]  Noah D. Goodman,et al.  A Bayesian Model of the Acquisition of Compositional Semantics , 2008 .

[7]  Laura A. Dabbish,et al.  Labeling images with a computer game , 2004, AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors.

[8]  Deb Roy,et al.  Semiotic schemas: A framework for grounding language in action and perception , 2005, Artif. Intell..

[9]  Michael Fleischman,et al.  Why Verbs are Harder to Learn than Nouns: Initial Insights from a Computational Model of Intention Recognition in Situated Word Learning , 2005 .

[10]  Jeff Orkin,et al.  The Restaurant Game: Learning Social Behavior and Language from Thousands of Players Online , 2008, J. Game Dev..

[11]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[12]  Albert Gatt,et al.  Generating Referring Expressions in Context: The GREC Task Evaluation Challenges , 2010, Empirical Methods in Natural Language Generation.

[13]  Michael C. Frank,et al.  PSYCHOLOGICAL SCIENCE Research Article Using Speakers ’ Referential Intentions to Model Early Cross-Situational Word Learning , 2022 .

[14]  Jeff Orkin,et al.  Learning Meanings of Words and Constructions, Grounded in a Virtual Game , 2010, KONVENS.

[15]  Jeff Orkin,et al.  Automatic learning and generation of social behavior from collective human gameplay , 2009, AAMAS.

[16]  Hans Uszkoreit,et al.  Talking NPCs in a Virtual Game World , 2010, ACL.

[17]  Paul R. Cohen,et al.  Wubble World , 2007, AIIDE.

[18]  Roger C. Schank,et al.  Scripts, plans, goals and understanding: an inquiry into human knowledge structures , 1978 .

[19]  Eytan Ruppin,et al.  Unsupervised learning of natural languages , 2006 .

[20]  L. Steels Evolving grounded communication for robots , 2003, Trends in Cognitive Sciences.

[21]  Jeff Orkin,et al.  Semi-automatic task recognition for interactive narratives with EAT & RUN , 2010, FDG.