3,932 research outputs found
A Machine Learning Approach For Opinion Holder Extraction In Arabic Language
Opinion mining aims at extracting useful subjective information from reliable
amounts of text. Opinion mining holder recognition is a task that has not been
considered yet in Arabic Language. This task essentially requires deep
understanding of clauses structures. Unfortunately, the lack of a robust,
publicly available, Arabic parser further complicates the research. This paper
presents a leading research for the opinion holder extraction in Arabic news
independent from any lexical parsers. We investigate constructing a
comprehensive feature set to compensate the lack of parsing structural
outcomes. The proposed feature set is tuned from English previous works coupled
with our proposed semantic field and named entities features. Our feature
analysis is based on Conditional Random Fields (CRF) and semi-supervised
pattern recognition techniques. Different research models are evaluated via
cross-validation experiments achieving 54.03 F-measure. We publicly release our
own research outcome corpus and lexicon for opinion mining community to
encourage further research
A Grammatical Inference Approach to Language-Based Anomaly Detection in XML
False-positives are a problem in anomaly-based intrusion detection systems.
To counter this issue, we discuss anomaly detection for the eXtensible Markup
Language (XML) in a language-theoretic view. We argue that many XML-based
attacks target the syntactic level, i.e. the tree structure or element content,
and syntax validation of XML documents reduces the attack surface. XML offers
so-called schemas for validation, but in real world, schemas are often
unavailable, ignored or too general. In this work-in-progress paper we describe
a grammatical inference approach to learn an automaton from example XML
documents for detecting documents with anomalous syntax.
We discuss properties and expressiveness of XML to understand limits of
learnability. Our contributions are an XML Schema compatible lexical datatype
system to abstract content in XML and an algorithm to learn visibly pushdown
automata (VPA) directly from a set of examples. The proposed algorithm does not
require the tree representation of XML, so it can process large documents or
streams. The resulting deterministic VPA then allows stream validation of
documents to recognize deviations in the underlying tree structure or
datatypes.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and
Countermeasures ECTCM 201
Dublin City University at QA@CLEF 2008
We describe our participation in Multilingual Question Answering at CLEF 2008 using German and English as our source and target languages respectively. The system was built using UIMA (Unstructured Information Management Architecture) as underlying framework
Multilingual domain modeling in Twenty-One: automatic creation of a bi-directional translation lexicon from a parallel corpus
Within the project Twenty-One, which aims at the effective dissemination of information on ecology and sustainable development, a sytem is developed that supports cross-language information retrieval in any of the four languages Dutch, English, French and German. Knowledge of this application domain is needed to enhance existing translation resources for the purpose of lexical disambiguation. This paper describes an algorithm for the automated acquisition of a translation lexicon from a parallel corpus. New about the presented algorithm is the statistical language model used. Because the algorithm is based on a symmetric translation model it becomes possible to identify one-to-many and many-to-one relations between words of a language pair. We claim that the presented method has two advantages over algorithms that have been published before. Firstly, because the translation model is more powerful, the resulting bilingual lexicon will be more accurate. Secondly, the resulting bilingual lexicon can be used to translate in both directions between a language pair. Different versions of the algorithm were evaluated on the Dutch and English version of the Agenda 21 corpus, which is a UN document on the application domain of sustainable development
Precise n-gram Probabilities from Stochastic Context-free Grammars
We present an algorithm for computing n-gram probabilities from stochastic
context-free grammars, a procedure that can alleviate some of the standard
problems associated with n-grams (estimation from sparse data, lack of
linguistic structure, among others). The method operates via the computation of
substring expectations, which in turn is accomplished by solving systems of
linear equations derived from the grammar. We discuss efficient implementation
of the algorithm and report our practical experience with it.Comment: 12 pages, to appear in ACL-9
Interactive specification acquisition via scenarios: A proposal
Some reactive systems are most naturally specified by giving large collections of behavior scenarios. These collections not only specify the behavior of the system, but also provide good test suites for validating the implemented system. Due to the complexity of the systems and the number of scenarios, however, it appears that automated assistance is necessary to make this software development process workable. Interactive Specification Acquisition Tool (ISAT) is a proposed interactive system for supporting the acquisition and maintenance of a formal system specification from scenarios, as well as automatic synthesis of control code and automated test generation. This paper discusses the background, motivation, proposed functions, and implementation status of ISAT
Subjects, Models, Languages, Transformations
Discussions about model-driven approaches tend to be hampered by terminological confusion. This is at least partially caused by a lack of formal precision in defining the basic concepts, including that of "model" and "thing being modelled" - which we call subject in this paper. We propose a minimal criterion that a model should fulfill: essentially, it should come equipped with a clear and unambiguous membership test; in other words, a notion of which subjects it models. We then go on to discuss a certain class of models of models that we call languages, which apart from defining their own membership test also determine membership of their members. Finally, we introduce transformations on each of these layers: a subject transformation is essentially a pair of subjects, a model transformation is both a pair of models and a model of pairs (namely, subject transformations), and a language transformation is both a pair of languages and a language of model transformations. We argue that our framework has the benefits of formal precision (there can be no doubt about whether something satifies our criteria for being a model, a language or a transformation) and minimality (it is hard to imagine a case of modelling or transformation not having the characterstics that we propose)
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