2,270 research outputs found
Effective Representation for Easy-First Dependency Parsing
Easy-first parsing relies on subtree re-ranking to build the complete parse
tree. Whereas the intermediate state of parsing processing is represented by
various subtrees, whose internal structural information is the key lead for
later parsing action decisions, we explore a better representation for such
subtrees. In detail, this work introduces a bottom-up subtree encoding method
based on the child-sum tree-LSTM. Starting from an easy-first dependency parser
without other handcraft features, we show that the effective subtree encoder
does promote the parsing process, and can make a greedy search easy-first
parser achieve promising results on benchmark treebanks compared to
state-of-the-art baselines. Furthermore, with the help of the current
pre-training language model, we further improve the state-of-the-art results of
the easy-first approach
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
We present a transition-based parser that jointly produces syntactic and
semantic dependencies. It learns a representation of the entire algorithm
state, using stack long short-term memories. Our greedy inference algorithm has
linear time, including feature extraction. On the CoNLL 2008--9 English shared
tasks, we obtain the best published parsing performance among models that
jointly learn syntax and semantics.Comment: Proceedings of CoNLL 2016; 13 pages, 5 figure
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a
sequential LSTM, to extract word order features while neglecting other valuable
syntactic information such as dependency graph or constituent trees. In this
paper, we first propose to use the \textit{syntactic graph} to represent three
types of syntactic information, i.e., word order, dependency and constituency
features. We further employ a graph-to-sequence model to encode the syntactic
graph and decode a logical form. Experimental results on benchmark datasets
show that our model is comparable to the state-of-the-art on Jobs640, ATIS and
Geo880. Experimental results on adversarial examples demonstrate the robustness
of the model is also improved by encoding more syntactic information.Comment: EMNLP'1
Deep Multitask Learning for Semantic Dependency Parsing
We present a deep neural architecture that parses sentences into three
semantic dependency graph formalisms. By using efficient, nearly arc-factored
inference and a bidirectional-LSTM composed with a multi-layer perceptron, our
base system is able to significantly improve the state of the art for semantic
dependency parsing, without using hand-engineered features or syntax. We then
explore two multitask learning approaches---one that shares parameters across
formalisms, and one that uses higher-order structures to predict the graphs
jointly. We find that both approaches improve performance across formalisms on
average, achieving a new state of the art. Our code is open-source and
available at https://github.com/Noahs-ARK/NeurboParser.Comment: Proceedings of ACL 201
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers
Solving mathematical word problems (MWPs) automatically is challenging,
primarily due to the semantic gap between human-readable words and
machine-understandable logics. Despite the long history dated back to the1960s,
MWPs have regained intensive attention in the past few years with the
advancement of Artificial Intelligence (AI). Solving MWPs successfully is
considered as a milestone towards general AI. Many systems have claimed
promising results in self-crafted and small-scale datasets. However, when
applied on large and diverse datasets, none of the proposed methods in the
literature achieves high precision, revealing that current MWP solvers still
have much room for improvement. This motivated us to present a comprehensive
survey to deliver a clear and complete picture of automatic math problem
solvers. In this survey, we emphasize on algebraic word problems, summarize
their extracted features and proposed techniques to bridge the semantic gap and
compare their performance in the publicly accessible datasets. We also cover
automatic solvers for other types of math problems such as geometric problems
that require the understanding of diagrams. Finally, we identify several
emerging research directions for the readers with interests in MWPs.Comment: 18 pages, 5 figure
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
An Empirical Comparison of Parsing Methods for Stanford Dependencies
Stanford typed dependencies are a widely desired representation of natural
language sentences, but parsing is one of the major computational bottlenecks
in text analysis systems. In light of the evolving definition of the Stanford
dependencies and developments in statistical dependency parsing algorithms,
this paper revisits the question of Cer et al. (2010): what is the tradeoff
between accuracy and speed in obtaining Stanford dependencies in particular? We
also explore the effects of input representations on this tradeoff:
part-of-speech tags, the novel use of an alternative dependency representation
as input, and distributional representaions of words. We find that direct
dependency parsing is a more viable solution than it was found to be in the
past. An accompanying software release can be found at:
http://www.ark.cs.cmu.edu/TBSDComment: 13 pages, 2 figure
Comparative Opinion Mining: A Review
Opinion mining refers to the use of natural language processing, text
analysis and computational linguistics to identify and extract subjective
information in textual material. Opinion mining, also known as sentiment
analysis, has received a lot of attention in recent times, as it provides a
number of tools to analyse the public opinion on a number of different topics.
Comparative opinion mining is a subfield of opinion mining that deals with
identifying and extracting information that is expressed in a comparative form
(e.g.~"paper X is better than the Y"). Comparative opinion mining plays a very
important role when ones tries to evaluate something, as it provides a
reference point for the comparison. This paper provides a review of the area of
comparative opinion mining. It is the first review that cover specifically this
topic as all previous reviews dealt mostly with general opinion mining. This
survey covers comparative opinion mining from two different angles. One from
perspective of techniques and the other from perspective of comparative opinion
elements. It also incorporates preprocessing tools as well as dataset that were
used by the past researchers that can be useful to the future researchers in
the field of comparative opinion mining
Head Automata and Bilingual Tiling: Translation with Minimal Representations
We present a language model consisting of a collection of costed
bidirectional finite state automata associated with the head words of phrases.
The model is suitable for incremental application of lexical associations in a
dynamic programming search for optimal dependency tree derivations. We also
present a model and algorithm for machine translation involving optimal
``tiling'' of a dependency tree with entries of a costed bilingual lexicon.
Experimental results are reported comparing methods for assigning cost
functions to these models. We conclude with a discussion of the adequacy of
annotated linguistic strings as representations for machine translation
Multitask Parsing Across Semantic Representations
The ability to consolidate information of different types is at the core of
intelligence, and has tremendous practical value in allowing learning for one
task to benefit from generalizations learned for others. In this paper we
tackle the challenging task of improving semantic parsing performance, taking
UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD)
parsing as auxiliary tasks. We experiment on three languages, using a uniform
transition-based system and learning architecture for all parsing tasks.
Despite notable conceptual, formal and domain differences, we show that
multitask learning significantly improves UCCA parsing in both in-domain and
out-of-domain settings.Comment: Accepted to ACL 201
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