11,960 research outputs found
Resource Constrained Structured Prediction
We study the problem of structured prediction under test-time budget
constraints. We propose a novel approach applicable to a wide range of
structured prediction problems in computer vision and natural language
processing. Our approach seeks to adaptively generate computationally costly
features during test-time in order to reduce the computational cost of
prediction while maintaining prediction performance. We show that training the
adaptive feature generation system can be reduced to a series of structured
learning problems, resulting in efficient training using existing structured
learning algorithms. This framework provides theoretical justification for
several existing heuristic approaches found in literature. We evaluate our
proposed adaptive system on two structured prediction tasks, optical character
recognition (OCR) and dependency parsing and show strong performance in
reduction of the feature costs without degrading accuracy
Parsing of Spoken Language under Time Constraints
Spoken language applications in natural dialogue settings place serious
requirements on the choice of processing architecture. Especially under adverse
phonetic and acoustic conditions parsing procedures have to be developed which
do not only analyse the incoming speech in a time-synchroneous and incremental
manner, but which are able to schedule their resources according to the varying
conditions of the recognition process. Depending on the actual degree of local
ambiguity the parser has to select among the available constraints in order to
narrow down the search space with as little effort as possible.
A parsing approach based on constraint satisfaction techniques is discussed.
It provides important characteristics of the desired real-time behaviour and
attempts to mimic some of the attention focussing capabilities of the human
speech comprehension mechanism.Comment: 19 pages, LaTe
Acquiring Word-Meaning Mappings for Natural Language Interfaces
This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted
Examples), that acquires a semantic lexicon from a corpus of sentences paired
with semantic representations. The lexicon learned consists of phrases paired
with meaning representations. WOLFIE is part of an integrated system that
learns to transform sentences into representations such as logical database
queries. Experimental results are presented demonstrating WOLFIE's ability to
learn useful lexicons for a database interface in four different natural
languages. The usefulness of the lexicons learned by WOLFIE are compared to
those acquired by a similar system, with results favorable to WOLFIE. A second
set of experiments demonstrates WOLFIE's ability to scale to larger and more
difficult, albeit artificially generated, corpora. In natural language
acquisition, it is difficult to gather the annotated data needed for supervised
learning; however, unannotated data is fairly plentiful. Active learning
methods attempt to select for annotation and training only the most informative
examples, and therefore are potentially very useful in natural language
applications. However, most results to date for active learning have only
considered standard classification tasks. To reduce annotation effort while
maintaining accuracy, we apply active learning to semantic lexicons. We show
that active learning can significantly reduce the number of annotated examples
required to achieve a given level of performance
Evaluation of the NLP Components of the OVIS2 Spoken Dialogue System
The NWO Priority Programme Language and Speech Technology is a 5-year
research programme aiming at the development of spoken language information
systems. In the Programme, two alternative natural language processing (NLP)
modules are developed in parallel: a grammar-based (conventional, rule-based)
module and a data-oriented (memory-based, stochastic, DOP) module. In order to
compare the NLP modules, a formal evaluation has been carried out three years
after the start of the Programme. This paper describes the evaluation procedure
and the evaluation results. The grammar-based component performs much better
than the data-oriented one in this comparison.Comment: Proceedings of CLIN 9
From Query to Usable Code: An Analysis of Stack Overflow Code Snippets
Enriched by natural language texts, Stack Overflow code snippets are an
invaluable code-centric knowledge base of small units of source code. Besides
being useful for software developers, these annotated snippets can potentially
serve as the basis for automated tools that provide working code solutions to
specific natural language queries.
With the goal of developing automated tools with the Stack Overflow snippets
and surrounding text, this paper investigates the following questions: (1) How
usable are the Stack Overflow code snippets? and (2) When using text search
engines for matching on the natural language questions and answers around the
snippets, what percentage of the top results contain usable code snippets?
A total of 3M code snippets are analyzed across four languages: C\#, Java,
JavaScript, and Python. Python and JavaScript proved to be the languages for
which the most code snippets are usable. Conversely, Java and C\# proved to be
the languages with the lowest usability rate. Further qualitative analysis on
usable Python snippets shows the characteristics of the answers that solve the
original question. Finally, we use Google search to investigate the alignment
of usability and the natural language annotations around code snippets, and
explore how to make snippets in Stack Overflow an adequate base for future
automatic program generation.Comment: 13th IEEE/ACM International Conference on Mining Software
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