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(Towards) a TTR parser: from strings of utterance events to types (2016)

Type Theory with Records (TTR) is a formal semantic framework that allows representing meaning closely related to action and perception. As such, we argued [1], it is ideally suited as a unified knowledge representation system for situated dialogue systems. In understanding language, two kinds of events are involved: events in the world and speech events of utterances. Recognising the former as types allows us to model the sense and reference of words, recognising the latter as types allows us to model syntactic structure of linguistic utterances [2].

The primary goal of the project is to explore parsing open text (which may be fragmented and incomplete, i.e. dialogue) into record-type representations which are represented as feature structures. The task might be accomplished in several different ways, (i) for example exploring shallow information extraction techniques can be used to identify entities and events in the text; (ii) adopting existing semantic parsers (e.g. the C&C tools for CCG or the MALT parser for dependency parses) to rewrite the output into the desired type representations; (iii) implementing new independent semantic parsing techniques that would return types directly. As types will represent discourse rather than isolated sentences, one could/would also explore different discourse referent/pronoun resolution methods and named entity identification.

The next step... "(Towards a) TTR parser: from types to perception" Once having type representations of linguistic events, how can they be linked to what we perceive? See the proposal "Situated Learning Agents" http://clt.gu.se/masterthesisproposal/situated-learning-agents"

[1] http://gup.ub.gu.se/publication/190853

[2] http://gup.ub.gu.se/publication/205229

Recommended skills:

Good Python programming skills both for processing text and of logic formalisms.


Simon Dobnik, FLoV and other members of Dialogue Technology Lab or Centre for Linguistic Theory and Studies in Probability (CLASP).

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Page updated: 2016-11-15 11:12

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