3,721 research outputs found
A geo-temporal information extraction service for processing descriptive metadata in digital libraries
In the context of digital map libraries, resources are usually described according to metadata records that define the relevant subject, location, time-span, format and keywords. On what concerns locations and time-spans, metadata records are often incomplete or they provide information in a way that is not machine-understandable (e.g. textual descriptions). This paper presents techniques for extracting geotemporal information from text, using relatively simple text mining methods that leverage on a Web gazetteer service. The idea is to go from human-made geotemporal referencing (i.e. using place and period names in textual expressions) into geo-spatial coordinates and time-spans. A prototype system, implementing the proposed methods, is described in detail. Experimental results demonstrate the efficiency and accuracy of the proposed approaches
Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration
Cross-language information retrieval (CLIR), where queries and documents are
in different languages, has of late become one of the major topics within the
information retrieval community. This paper proposes a Japanese/English CLIR
system, where we combine a query translation and retrieval modules. We
currently target the retrieval of technical documents, and therefore the
performance of our system is highly dependent on the quality of the translation
of technical terms. However, the technical term translation is still
problematic in that technical terms are often compound words, and thus new
terms are progressively created by combining existing base words. In addition,
Japanese often represents loanwords based on its special phonogram.
Consequently, existing dictionaries find it difficult to achieve sufficient
coverage. To counter the first problem, we produce a Japanese/English
dictionary for base words, and translate compound words on a word-by-word
basis. We also use a probabilistic method to resolve translation ambiguity. For
the second problem, we use a transliteration method, which corresponds words
unlisted in the base word dictionary to their phonetic equivalents in the
target language. We evaluate our system using a test collection for CLIR, and
show that both the compound word translation and transliteration methods
improve the system performance
User evaluation of a pilot terminologies server for a distributed multi-scheme environment
The present paper reports on a user-centred evaluation of a pilot terminology service developed as part of the High Level Thesaurus (HILT) project at the Centre for Digital Library Research (CDLR) in the University of Strathclyde in Glasgow. The pilot terminology service was developed as an experimental platform to investigate issues relating to mapping between various subject schemes, namely Dewey Decimal Classification (DDC), Library of Congress Subject Headings (LCSH), the Unesco thesaurus, and the MeSH thesaurus, in order to cater for cross-browsing and cross-searching across distributed digital collections and services. The aim of the evaluation reported here was to investigate users' thought processes, perceptions, and attitudes towards the pilot terminology service and to identify user requirements for developing a full-blown pilot terminology service
Onebox: Free-Text Interfaces as an Alternative to Complex Web Forms
This paper investigates the problem of translating free-text\ud
queries into key-value pairs as an alternative means for searching `behind' web forms. We introduce a novel specication language for specifying free-text interfaces, and report the results of a user study where we evaluated our prototype in a travel planner scenario. Our results show that users prefer this free-text interface over the original web form and that they are about 9% faster on average at completing their search tasks
Word-Entity Duet Representations for Document Ranking
This paper presents a word-entity duet framework for utilizing knowledge
bases in ad-hoc retrieval. In this work, the query and documents are modeled by
word-based representations and entity-based representations. Ranking features
are generated by the interactions between the two representations,
incorporating information from the word space, the entity space, and the
cross-space connections through the knowledge graph. To handle the
uncertainties from the automatically constructed entity representations, an
attention-based ranking model AttR-Duet is developed. With back-propagation
from ranking labels, the model learns simultaneously how to demote noisy
entities and how to rank documents with the word-entity duet. Evaluation
results on TREC Web Track ad-hoc task demonstrate that all of the four-way
interactions in the duet are useful, the attention mechanism successfully
steers the model away from noisy entities, and together they significantly
outperform both word-based and entity-based learning to rank systems
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Entity linking has recently been the subject of a significant body of
research. Currently, the best performing approaches rely on trained
mono-lingual models. Porting these approaches to other languages is
consequently a difficult endeavor as it requires corresponding training data
and retraining of the models. We address this drawback by presenting a novel
multilingual, knowledge-based agnostic and deterministic approach to entity
linking, dubbed MAG. MAG is based on a combination of context-based retrieval
on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data
sets and in 7 languages. Our results show that the best approach trained on
English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse
on datasets in other languages. MAG, on the other hand, achieves
state-of-the-art performance on English datasets and reaches a micro F-measure
that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc
Query Expansion with Locally-Trained Word Embeddings
Continuous space word embeddings have received a great deal of attention in
the natural language processing and machine learning communities for their
ability to model term similarity and other relationships. We study the use of
term relatedness in the context of query expansion for ad hoc information
retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when
trained globally, underperform corpus and query specific embeddings for
retrieval tasks. These results suggest that other tasks benefiting from global
embeddings may also benefit from local embeddings
Matching MEDLINE/PubMed data with Web of Science (WoS): a routine in R language
We present a novel routine, namely medlineR, based on R language, that enables the user to match data from MEDLINE/PubMed with records indexed in the ISI Web of Science (WoS) database. The matching allows exploiting the rich and controlled vocabulary of Medical Sub- ject Headings (MeSH) of MEDLINE/PubMed with additional fields of WoS. The integration provides data (e.g. citation data, list of cited reference, list of the addresses of authorsâ host organisations, WoS subject categories) to perform a variety of scientometric analyses. This brief communication describes medlineR, the methodology on which it relies, and the steps the user should follow to perform the matching across the two databases. In order to specify the differences from Leydesdorff and Opthof (2013), we conclude the brief communication by testing the routine on the case of the "Burgada Syndrome"
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