91,775 research outputs found
Intelligent Self-Repairable Web Wrappers
The amount of information available on the Web grows at an incredible high rate. Systems and procedures devised to extract these data from Web sources already exist, and different approaches and techniques have been investigated during the last years. On the one hand, reliable solutions should provide robust algorithms of Web data mining which could automatically face possible malfunctioning or failures. On the other, in literature there is a lack of solutions about the maintenance of these systems. Procedures that extract Web data may be strictly interconnected with the structure of the data source itself; thus, malfunctioning or acquisition of corrupted data could be caused, for example, by structural modifications of data sources brought by their owners. Nowadays, verification of data integrity and maintenance are mostly manually managed, in order to ensure that these systems work correctly and reliably. In this paper we propose a novel approach to create procedures able to extract data from Web sources -- the so called Web wrappers -- which can face possible malfunctioning caused by modifications of the structure of the data source, and can automatically repair themselves.\u
Design of Automatically Adaptable Web Wrappers
Nowadays, the huge amount of information distributed through the Web motivates studying techniques to\ud
be adopted in order to extract relevant data in an efficient and reliable way. Both academia and enterprises\ud
developed several approaches of Web data extraction, for example using techniques of artificial intelligence or\ud
machine learning. Some commonly adopted procedures, namely wrappers, ensure a high degree of precision\ud
of information extracted from Web pages, and, at the same time, have to prove robustness in order not to\ud
compromise quality and reliability of data themselves.\ud
In this paper we focus on some experimental aspects related to the robustness of the data extraction process\ud
and the possibility of automatically adapting wrappers. We discuss the implementation of algorithms for\ud
finding similarities between two different version of a Web page, in order to handle modifications, avoiding\ud
the failure of data extraction tasks and ensuring reliability of information extracted. Our purpose is to evaluate\ud
performances, advantages and draw-backs of our novel system of automatic wrapper adaptation
Automatic Wrapper Adaptation by Tree Edit Distance Matching
Information distributed through the Web keeps growing faster day by day,\ud
and for this reason, several techniques for extracting Web data have been suggested\ud
during last years. Often, extraction tasks are performed through so called wrappers,\ud
procedures extracting information from Web pages, e.g. implementing logic-based\ud
techniques. Many fields of application today require a strong degree of robustness\ud
of wrappers, in order not to compromise assets of information or reliability of data\ud
extracted.\ud
Unfortunately, wrappers may fail in the task of extracting data from a Web page, if\ud
its structure changes, sometimes even slightly, thus requiring the exploiting of new\ud
techniques to be automatically held so as to adapt the wrapper to the new structure\ud
of the page, in case of failure. In this work we present a novel approach of automatic wrapper adaptation based on the measurement of similarity of trees through\ud
improved tree edit distance matching techniques
Adapting a general parser to a sublanguage
In this paper, we propose a method to adapt a general parser (Link Parser) to
sublanguages, focusing on the parsing of texts in biology. Our main proposal is
the use of terminology (identication and analysis of terms) in order to reduce
the complexity of the text to be parsed. Several other strategies are explored
and finally combined among which text normalization, lexicon and
morpho-guessing module extensions and grammar rules adaptation. We compare the
parsing results before and after these adaptations
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
Lexical Adaptation of Link Grammar to the Biomedical Sublanguage: a Comparative Evaluation of Three Approaches
We study the adaptation of Link Grammar Parser to the biomedical sublanguage
with a focus on domain terms not found in a general parser lexicon. Using two
biomedical corpora, we implement and evaluate three approaches to addressing
unknown words: automatic lexicon expansion, the use of morphological clues, and
disambiguation using a part-of-speech tagger. We evaluate each approach
separately for its effect on parsing performance and consider combinations of
these approaches. In addition to a 45% increase in parsing efficiency, we find
that the best approach, incorporating information from a domain part-of-speech
tagger, offers a statistically signicant 10% relative decrease in error. The
adapted parser is available under an open-source license at
http://www.it.utu.fi/biolg
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
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