KELLER

Language and Computation
AUTOMATED ACQUISITION OF SYNTAX AND PARSING

Workshop

BILL KELLER

School of Cognitive and Computing Sciences, University of Sussex

Second week
billk@cogs.susx.ac.uk

Course description

Robust parsing of unrestricted text remains an elusive goal of natural language processing. Traditional parsing systems based on hand-crafted generative grammars are time-consuming to produce, difficult to maintain and suffer from the problem of under-generation. There is growing interest in techniques for acquiring syntactic rules or parsing strategies from language samples.

This workshop is aimed at young researchers investigating the automatic or semi-automatic acquisition of syntax or parsing procedures from language data. Topics of interest include:

Statistical and symbolic approaches to grammatical inference
(e.g. Bayesian inference, expectation maximization, explanation-based learning, memory-based learning, MDL, maximum entropy models, inductive logic programming, decision trees, etc.);
Machine learning of parse strategies and decisions (e.g. learning shift-reduce parsers, "history-based" parsing, learning syntactic disambiguation rules);
Theoretical or empirical studies of approaches to the automatic acquisition of grammar or syntactic processing;
Empirical methods in grammar development and syntactic processing (e.g. data-oriented parsing, acquiring grammars from tree-banks learning sub-categorization frames)

Invited speakers:

Thorsten Brants (University of Saarbruecken, Germany)
Sabine Buchholz (The Netherlands)
Anja Belz (University of Sussex, United Kingdom)
Martin Eineborg ( Sweden)
Berkan Eskikaya (University of Sussex, United Kingdom)
Marsal Gavalda (United States of America)
Bernd Geistert (University of Erlangen, Germany)
Anna Korhonen (University of Cambridge, United Kingdom)
Nikolaj Lindberg (Sweden)
Markus Schulze (University of Erlangen, Germany)
Wojciech Skut (University of Saarbruecken, Germany)
Hugo ter Doest (University of Twente, The Netherlands)

Prerequisites
None

Literature
No specific recommendation

HOME PROGRAMME CONTACT REGISTRATION

Language and Computation	AUTOMATED ACQUISITION OF SYNTAX AND PARSING
Workshop	BILL KELLER School of Cognitive and Computing Sciences, University of Sussex
Second week	billk@cogs.susx.ac.uk
Course description	Robust parsing of unrestricted text remains an elusive goal of natural language processing. Traditional parsing systems based on hand-crafted generative grammars are time-consuming to produce, difficult to maintain and suffer from the problem of under-generation. There is growing interest in techniques for acquiring syntactic rules or parsing strategies from language samples. This workshop is aimed at young researchers investigating the automatic or semi-automatic acquisition of syntax or parsing procedures from language data. Topics of interest include: Statistical and symbolic approaches to grammatical inference (e.g. Bayesian inference, expectation maximization, explanation-based learning, memory-based learning, MDL, maximum entropy models, inductive logic programming, decision trees, etc.); Machine learning of parse strategies and decisions (e.g. learning shift-reduce parsers, "history-based" parsing, learning syntactic disambiguation rules); Theoretical or empirical studies of approaches to the automatic acquisition of grammar or syntactic processing; Empirical methods in grammar development and syntactic processing (e.g. data-oriented parsing, acquiring grammars from tree-banks learning sub-categorization frames) Invited speakers: Thorsten Brants (University of Saarbruecken, Germany) Sabine Buchholz (The Netherlands) Anja Belz (University of Sussex, United Kingdom) Martin Eineborg ( Sweden) Berkan Eskikaya (University of Sussex, United Kingdom) Marsal Gavalda (United States of America) Bernd Geistert (University of Erlangen, Germany) Anna Korhonen (University of Cambridge, United Kingdom) Nikolaj Lindberg (Sweden) Markus Schulze (University of Erlangen, Germany) Wojciech Skut (University of Saarbruecken, Germany) Hugo ter Doest (University of Twente, The Netherlands)
Prerequisites	None
Literature	No specific recommendation