8,305 research outputs found
Robust Processing of Natural Language
Previous approaches to robustness in natural language processing usually
treat deviant input by relaxing grammatical constraints whenever a successful
analysis cannot be provided by ``normal'' means. This schema implies, that
error detection always comes prior to error handling, a behaviour which hardly
can compete with its human model, where many erroneous situations are treated
without even noticing them.
The paper analyses the necessary preconditions for achieving a higher degree
of robustness in natural language processing and suggests a quite different
approach based on a procedure for structural disambiguation. It not only offers
the possibility to cope with robustness issues in a more natural way but
eventually might be suited to accommodate quite different aspects of robust
behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro,
pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th
German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture
Notes in Computer Science, Springer 199
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
Using parse features for preposition selection and error detection
We evaluate the effect of adding parse features to a leading model of preposition usage. Results show a significant improvement in the preposition selection task on
native speaker text and a modest increment in precision and recall in an ESL error detection task. Analysis of the parser output indicates that it is robust enough in the face
of noisy non-native writing to extract useful information
Latent Tree Language Model
In this paper we introduce Latent Tree Language Model (LTLM), a novel
approach to language modeling that encodes syntax and semantics of a given
sentence as a tree of word roles.
The learning phase iteratively updates the trees by moving nodes according to
Gibbs sampling. We introduce two algorithms to infer a tree for a given
sentence. The first one is based on Gibbs sampling. It is fast, but does not
guarantee to find the most probable tree. The second one is based on dynamic
programming. It is slower, but guarantees to find the most probable tree. We
provide comparison of both algorithms.
We combine LTLM with 4-gram Modified Kneser-Ney language model via linear
interpolation. Our experiments with English and Czech corpora show significant
perplexity reductions (up to 46% for English and 49% for Czech) compared with
standalone 4-gram Modified Kneser-Ney language model.Comment: Accepted to EMNLP 201
A Web-Based Tool for Analysing Normative Documents in English
Our goal is to use formal methods to analyse normative documents written in
English, such as privacy policies and service-level agreements. This requires
the combination of a number of different elements, including information
extraction from natural language, formal languages for model representation,
and an interface for property specification and verification. We have worked on
a collection of components for this task: a natural language extraction tool, a
suitable formalism for representing such documents, an interface for building
models in this formalism, and methods for answering queries asked of a given
model. In this work, each of these concerns is brought together in a web-based
tool, providing a single interface for analysing normative texts in English.
Through the use of a running example, we describe each component and
demonstrate the workflow established by our tool
Extracting Formal Models from Normative Texts
We are concerned with the analysis of normative texts - documents based on
the deontic notions of obligation, permission, and prohibition. Our goal is to
make queries about these notions and verify that a text satisfies certain
properties concerning causality of actions and timing constraints. This
requires taking the original text and building a representation (model) of it
in a formal language, in our case the C-O Diagram formalism. We present an
experimental, semi-automatic aid that helps to bridge the gap between a
normative text in natural language and its C-O Diagram representation. Our
approach consists of using dependency structures obtained from the
state-of-the-art Stanford Parser, and applying our own rules and heuristics in
order to extract the relevant components. The result is a tabular data
structure where each sentence is split into suitable fields, which can then be
converted into a C-O Diagram. The process is not fully automatic however, and
some post-editing is generally required of the user. We apply our tool and
perform experiments on documents from different domains, and report an initial
evaluation of the accuracy and feasibility of our approach.Comment: Extended version of conference paper at the 21st International
Conference on Applications of Natural Language to Information Systems (NLDB
2016). arXiv admin note: substantial text overlap with arXiv:1607.0148
Incremental Interpretation: Applications, Theory, and Relationship to Dynamic Semantics
Why should computers interpret language incrementally? In recent years
psycholinguistic evidence for incremental interpretation has become more and
more compelling, suggesting that humans perform semantic interpretation before
constituent boundaries, possibly word by word. However, possible computational
applications have received less attention. In this paper we consider various
potential applications, in particular graphical interaction and dialogue. We
then review the theoretical and computational tools available for mapping from
fragments of sentences to fully scoped semantic representations. Finally, we
tease apart the relationship between dynamic semantics and incremental
interpretation.Comment: Procs. of COLING 94, LaTeX (2.09 preferred), 8 page
Crowdsourced real-world sensing: sentiment analysis and the real-time web
The advent of the real-time web is proving both challeng-
ing and at the same time disruptive for a number of areas of research,
notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis
and discusses the motivations and challenges behind such a direction
- …