509 research outputs found
Thread Reconstruction in Conversational Data using Neural Coherence Models
Discussion forums are an important source of information. They are often used
to answer specific questions a user might have and to discover more about a
topic of interest. Discussions in these forums may evolve in intricate ways,
making it difficult for users to follow the flow of ideas. We propose a novel
approach for automatically identifying the underlying thread structure of a
forum discussion. Our approach is based on a neural model that computes
coherence scores of possible reconstructions and then selects the highest
scoring, i.e., the most coherent one. Preliminary experiments demonstrate
promising results outperforming a number of strong baseline methods.Comment: Neu-IR: Workshop on Neural Information Retrieval 201
Improving Semantic Composition with Offset Inference
Count-based distributional semantic models suffer from sparsity due to
unobserved but plausible co-occurrences in any text collection. This problem is
amplified for models like Anchored Packed Trees (APTs), that take the
grammatical type of a co-occurrence into account. We therefore introduce a
novel form of distributional inference that exploits the rich type structure in
APTs and infers missing data by the same mechanism that is used for semantic
composition.Comment: to appear at ACL 2017 (short papers
Bare-Bones Dependency Parsing — A Case for Occam's Razor?
Proceedings of the 18th Nordic Conference of Computational Linguistics
NODALIDA 2011.
Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa.
NEALT Proceedings Series, Vol. 11 (2011), 6-11.
© 2011 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/16955
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