310 research outputs found
Toponym Disambiguation in Information Retrieval
In recent years, geography has acquired a great importance in the context of
Information Retrieval (IR) and, in general, of the automated processing of
information in text. Mobile devices that are able to surf the web and at the
same time inform about their position are now a common reality, together
with applications that can exploit this data to provide users with locally
customised information, such as directions or advertisements. Therefore,
it is important to deal properly with the geographic information that is
included in electronic texts. The majority of such kind of information is
contained as place names, or toponyms.
Toponym ambiguity represents an important issue in Geographical Information
Retrieval (GIR), due to the fact that queries are geographically constrained.
There has been a struggle to nd speci c geographical IR methods
that actually outperform traditional IR techniques. Toponym ambiguity
may constitute a relevant factor in the inability of current GIR systems to
take advantage from geographical knowledge. Recently, some Ph.D. theses
have dealt with Toponym Disambiguation (TD) from di erent perspectives,
from the development of resources for the evaluation of Toponym Disambiguation
(Leidner (2007)) to the use of TD to improve geographical scope
resolution (Andogah (2010)). The Ph.D. thesis presented here introduces
a TD method based on WordNet and carries out a detailed study of the
relationship of Toponym Disambiguation to some IR applications, such as
GIR, Question Answering (QA) and Web retrieval.
The work presented in this thesis starts with an introduction to the applications
in which TD may result useful, together with an analysis of the
ambiguity of toponyms in news collections. It could not be possible to
study the ambiguity of toponyms without studying the resources that are
used as placename repositories; these resources are the equivalent to language
dictionaries, which provide the di erent meanings of a given word.Buscaldi, D. (2010). Toponym Disambiguation in Information Retrieval [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8912Palanci
Toponym extraction and disambiguation enhancement using loops of feedback
Toponym extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. This paper addresses two problems with toponym extraction and disambiguation. First, almost no existing works examine the extraction and disambiguation interdependency. Second, existing disambiguation techniques mostly take as input extracted named entities without considering the uncertainty and imperfection of the extraction process. In this paper we aim to investigate both avenues and to show that explicit handling of the uncertainty of annotation has much potential for making both extraction and disambiguation more robust. We conducted experiments with a set of holiday home descriptions with the aim to extract and disambiguate toponyms. We show that the extraction confidence probabilities are useful in enhancing the effectiveness of disambiguation. Reciprocally, retraining the extraction models with information automatically derived from the disambiguation results, improves the extraction models. This mutual reinforcement is shown to even have an effect after several automatic iterations
Named Entity Extraction and Disambiguation: The Reinforcement Effect.
Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. Although these topics are highly dependent, almost no existing works examine this dependency. It is the aim of this paper to examine the dependency and show how one affects the other, and vice versa. We conducted experiments with a set of descriptions of holiday homes with the aim to extract and disambiguate toponyms as a representative example of named entities. We experimented with three approaches for disambiguation with the purpose to infer the country of the holiday home. We examined how the effectiveness of extraction influences the effectiveness of disambiguation, and reciprocally, how filtering out ambiguous names (an activity that depends on the disambiguation process) improves the effectiveness of extraction. Since this, in turn, may improve the effectiveness of disambiguation again, it shows that extraction and disambiguation may reinforce each other.\u
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
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