39,545 research outputs found
Seeding statistical machine translation with translation memory output through tree-based structural alignment
With the steadily increasing demand for high-quality translation, the localisation industry is constantly searching for technologies that would increase translator
throughput, with the current focus on the use of high-quality Statistical Machine Translation (SMT) as a supplement to the established Translation Memory (TM)
technology. In this paper we present a novel modular approach that utilises state-of-the-art sub-tree alignment to pick out pre-translated segments from a TM match and seed with them an SMT system to produce a final translation. We show that the presented system can outperform pure SMT when a good TM match is found. It can also be used in a Computer-Aided Translation (CAT) environment to present almost perfect translations to the human user with markup highlighting the segments of the translation that need to be checked manually for correctness
Three New Probabilistic Models for Dependency Parsing: An Exploration
After presenting a novel O(n^3) parsing algorithm for dependency grammar, we
develop three contrasting ways to stochasticize it. We propose (a) a lexical
affinity model where words struggle to modify each other, (b) a sense tagging
model where words fluctuate randomly in their selectional preferences, and (c)
a generative model where the speaker fleshes out each word's syntactic and
conceptual structure without regard to the implications for the hearer. We also
give preliminary empirical results from evaluating the three models' parsing
performance on annotated Wall Street Journal training text (derived from the
Penn Treebank). In these results, the generative (i.e., top-down) model
performs significantly better than the others, and does about equally well at
assigning part-of-speech tags.Comment: 6 pages, LaTeX 2.09 packaged with 4 .eps files, also uses colap.sty
and acl.bs
Generating collaborative systems for digital libraries: A model-driven approach
This is an open access article shared under a Creative Commons Attribution 3.0 Licence (http://creativecommons.org/licenses/by/3.0/). Copyright @ 2010 The Authors.The design and development of a digital library involves different stakeholders, such as: information architects, librarians, and domain experts, who need to agree on a common language to describe, discuss, and negotiate the services the library has to offer. To this end, high-level, language-neutral models have to be devised. Metamodeling techniques favor the definition of domainspecific visual languages through which stakeholders can share their views and directly manipulate representations of the domain entities. This paper describes CRADLE (Cooperative-Relational Approach to Digital Library Environments), a metamodel-based framework and visual language for the definition of notions and services related to the development of digital libraries. A collection of tools allows the automatic generation of several services, defined with the CRADLE visual language, and of the graphical user interfaces providing access to them for the final user. The effectiveness of the approach is illustrated by presenting digital libraries generated with CRADLE, while the CRADLE environment has been evaluated by using the cognitive dimensions framework
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
Attentive Tensor Product Learning
This paper proposes a new architecture - Attentive Tensor Product Learning
(ATPL) - to represent grammatical structures in deep learning models. ATPL is a
new architecture to bridge this gap by exploiting Tensor Product
Representations (TPR), a structured neural-symbolic model developed in
cognitive science, aiming to integrate deep learning with explicit language
structures and rules. The key ideas of ATPL are: 1) unsupervised learning of
role-unbinding vectors of words via TPR-based deep neural network; 2) employing
attention modules to compute TPR; and 3) integration of TPR with typical deep
learning architectures including Long Short-Term Memory (LSTM) and Feedforward
Neural Network (FFNN). The novelty of our approach lies in its ability to
extract the grammatical structure of a sentence by using role-unbinding
vectors, which are obtained in an unsupervised manner. This ATPL approach is
applied to 1) image captioning, 2) part of speech (POS) tagging, and 3)
constituency parsing of a sentence. Experimental results demonstrate the
effectiveness of the proposed approach
Structured Prediction of Sequences and Trees using Infinite Contexts
Linguistic structures exhibit a rich array of global phenomena, however
commonly used Markov models are unable to adequately describe these phenomena
due to their strong locality assumptions. We propose a novel hierarchical model
for structured prediction over sequences and trees which exploits global
context by conditioning each generation decision on an unbounded context of
prior decisions. This builds on the success of Markov models but without
imposing a fixed bound in order to better represent global phenomena. To
facilitate learning of this large and unbounded model, we use a hierarchical
Pitman-Yor process prior which provides a recursive form of smoothing. We
propose prediction algorithms based on A* and Markov Chain Monte Carlo
sampling. Empirical results demonstrate the potential of our model compared to
baseline finite-context Markov models on part-of-speech tagging and syntactic
parsing
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