12,701 research outputs found
Cross-lingual document retrieval categorisation and navigation based on distributed services
The widespread use of the Internet across countries has increased the need for access to document collections
that are often written in languages different from a user’s native language. In this paper we describe Clarity, a
Cross Language Information Retrieval (CLIR) system for English, Finnish, Swedish, Latvian and Lithuanian.
Clarity is a fully-fledged retrieval system that supports the user during the whole process of query formulation,
text retrieval and document browsing. We address four of the major aspects of Clarity: (i) the user-driven
methodology that formed the basis for the iterative design cycle and framework in the project, (ii) the system
architecture that was developed to support the interaction and coordination of Clarity’s distributed services, (iii)
the data resources and methods for query translation, and (iv) the support for Baltic languages. Clarity is an
example of a distributed CLIR system built with minimal translation resources and, to our knowledge, the only
such system that currently supports Baltic languages
Multilingual search for cultural heritage archives via combining multiple translation resources
The linguistic features of material in Cultural Heritage (CH) archives may be in various languages requiring a facility for effective multilingual search. The specialised
language often associated with CH content introduces problems for automatic translation to support search applications. The MultiMatch project is focused on enabling
users to interact with CH content across different media types and languages. We present results from a MultiMatch study exploring various translation techniques for
the CH domain. Our experiments examine translation techniques for the English language CLEF 2006 Cross-Language
Speech Retrieval (CL-SR) task using Spanish, French and German queries. Results compare effectiveness of our query
translation against a monolingual baseline and show improvement when combining a domain-specific translation lexicon with a standard machine translation system
A platform for discovering and sharing confidential ballistic crime data.
Criminal investigations generate large volumes of complex data that detectives have to analyse and understand. This data tends to be "siloed" within individual jurisdictions and re-using it in other investigations can be difficult. Investigations into trans-national crimes are hampered by the problem of discovering relevant data held by agencies in other countries and of sharing those data. Gun-crimes are one major type of incident that showcases this: guns are easily moved across borders and used in multiple crimes but finding that a weapon was used elsewhere in Europe is difficult. In this paper we report on the Odyssey Project, an EU-funded initiative to mine, manipulate and share data about weapons and crimes. The project demonstrates the automatic combining of data from disparate repositories for cross-correlation and automated analysis. The data arrive from different cultural/domains with multiple reference models using real-time data feeds and historical databases
SmartInt: Using Mined Attribute Dependencies to Integrate Fragmented Web Databases
Many web databases can be seen as providing partial and overlapping
information about entities in the world. To answer queries effectively, we need
to integrate the information about the individual entities that are fragmented
over multiple sources. At first blush this is just the inverse of traditional
database normalization problem - rather than go from a universal relation to
normalized tables, we want to reconstruct the universal relation given the
tables (sources). The standard way of reconstructing the entities will involve
joining the tables. Unfortunately, because of the autonomous and decentralized
way in which the sources are populated, they often do not have Primary Key -
Foreign Key relations. While tables may share attributes, naive joins over
these shared attributes can result in reconstruction of many spurious entities
thus seriously compromising precision. Our system, \smartint\ is aimed at
addressing the problem of data integration in such scenarios. Given a query,
our system uses the Approximate Functional Dependencies (AFDs) to piece
together a tree of relevant tables to answer it. The result tuples produced by
our system are able to strike a favorable balance between precision and recall
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
Data Cleaning: Problems and Current Approaches
We classify data quality problems that are addressed by data cleaning and provide an overview of the main solution approaches. Data cleaning is especially required when integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data warehouses, data cleaning is a major part of the so-called ETL process. We also discuss current tool support for data cleaning
- …