MOL - Association for Mathematics of Language

MOL
Association for Mathematics of Language

MOL is a special interest group of the Association for Computational Linguistics

Accepted Papers

9:00-9:30 Tim Hunter and Chris Dyer: Distributions on Minimalist Grammar Derivations
9:30-10:00 Meaghan Fowlie: Order and Optionality: Minimalist Grammars with Adjunction
10:00-10:30 Martin Berglund, Henrik Björklund and Frank Drewes: On the Parameterized Complexity of Linear Context-Free Rewriting Systems
11:00-11:30 Tim Fernando: Segmenting temporal intervals for tense and aspect
11:30-12:00 Stephen Clark, Bob Coecke and Mehrnoosh Sadrzadeh: The Frobenius Anatomy of Relative Pronouns
12:00-12:30 Jeffrey Heinz and Regine Lai: Vowel harmony and subsequentiality
14:00-14:30 Jeffery Heinz and Jim Rogers: Learning Subregular Classes of Languages with Factored Deterministic Automata
14:30-15:00 Andras Kornai, Attila Zseder and Gabor Recski: Structure learning in weighted languages
15:00-15:30 Eric Corlett and Gerald Penn: Why Letter Substitution Puzzles are Not Hard to Solve: A Case Study in Entropy and Probabilistic Search-Complexity
16:00-16:30 Spyros Martzoukos, Christophe Costa Florencio and Christof Monz: Investigating Connectivity and Consistency Criteria for Phrase Pair Extraction in Statistical Machine Translation
16:30-17:30 INVITED TALK Mark Johnson: Grammars and Topic Models

Th schedule will be harmonized with that of the other workshops so that breaks are at the same time.

Special Events

Invited Speaker: Mark Johnson

Grammars and Topic Models

Context-free grammars have been a cornerstone of theoretical computer science and computational linguistics since their inception over half a century ago. Topic models are a newer development in machine learning that play an important role in document analysis and information retrieval. It turns out there is a surprising connection between the two that suggests novel ways of extending both grammars and topic models. After explaining this connection, I go on to describe extensions which identify topical multi-word collocations and automatically learn the internal structure of named-entity phrases. These new models have applications in text data mining and information retrieval.

17:45 Business Meeting

MOL Meal Doodle for time and place!