408 research outputs found
Keystroke dynamics as signal for shallow syntactic parsing
Keystroke dynamics have been extensively used in psycholinguistic and writing
research to gain insights into cognitive processing. But do keystroke logs
contain actual signal that can be used to learn better natural language
processing models?
We postulate that keystroke dynamics contain information about syntactic
structure that can inform shallow syntactic parsing. To test this hypothesis,
we explore labels derived from keystroke logs as auxiliary task in a multi-task
bidirectional Long Short-Term Memory (bi-LSTM). Our results show promising
results on two shallow syntactic parsing tasks, chunking and CCG supertagging.
Our model is simple, has the advantage that data can come from distinct
sources, and produces models that are significantly better than models trained
on the text annotations alone.Comment: In COLING 201
A Transition-Based Directed Acyclic Graph Parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable
framework for semantic representation, which builds on extensive typological
work and supports rapid annotation. UCCA poses a challenge for existing parsing
techniques, as it exhibits reentrancy (resulting in DAG structures),
discontinuous structures and non-terminal nodes corresponding to complex
semantic units. To our knowledge, the conjunction of these formal properties is
not supported by any existing parser. Our transition-based parser, which uses a
novel transition set and features based on bidirectional LSTMs, has value not
just for UCCA parsing: its ability to handle more general graph structures can
inform the development of parsers for other semantic DAG structures, and in
languages that frequently use discontinuous structures.Comment: 16 pages; Accepted as long paper at ACL201
AMR Dependency Parsing with a Typed Semantic Algebra
We present a semantic parser for Abstract Meaning Representations which
learns to parse strings into tree representations of the compositional
structure of an AMR graph. This allows us to use standard neural techniques for
supertagging and dependency tree parsing, constrained by a linguistically
principled type system. We present two approximative decoding algorithms, which
achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see
https://acl2018.org/programme/papers/
- …