26,989 research outputs found
POS Tagging and its Applications for Mathematics
Content analysis of scientific publications is a nontrivial task, but a
useful and important one for scientific information services. In the Gutenberg
era it was a domain of human experts; in the digital age many machine-based
methods, e.g., graph analysis tools and machine-learning techniques, have been
developed for it. Natural Language Processing (NLP) is a powerful
machine-learning approach to semiautomatic speech and language processing,
which is also applicable to mathematics. The well established methods of NLP
have to be adjusted for the special needs of mathematics, in particular for
handling mathematical formulae. We demonstrate a mathematics-aware part of
speech tagger and give a short overview about our adaptation of NLP methods for
mathematical publications. We show the use of the tools developed for key
phrase extraction and classification in the database zbMATH
Artificial Sequences and Complexity Measures
In this paper we exploit concepts of information theory to address the
fundamental problem of identifying and defining the most suitable tools to
extract, in a automatic and agnostic way, information from a generic string of
characters. We introduce in particular a class of methods which use in a
crucial way data compression techniques in order to define a measure of
remoteness and distance between pairs of sequences of characters (e.g. texts)
based on their relative information content. We also discuss in detail how
specific features of data compression techniques could be used to introduce the
notion of dictionary of a given sequence and of Artificial Text and we show how
these new tools can be used for information extraction purposes. We point out
the versatility and generality of our method that applies to any kind of
corpora of character strings independently of the type of coding behind them.
We consider as a case study linguistic motivated problems and we present
results for automatic language recognition, authorship attribution and self
consistent-classification.Comment: Revised version, with major changes, of previous "Data Compression
approach to Information Extraction and Classification" by A. Baronchelli and
V. Loreto. 15 pages; 5 figure
On Quantifying Qualitative Geospatial Data: A Probabilistic Approach
Living in the era of data deluge, we have witnessed a web content explosion,
largely due to the massive availability of User-Generated Content (UGC). In
this work, we specifically consider the problem of geospatial information
extraction and representation, where one can exploit diverse sources of
information (such as image and audio data, text data, etc), going beyond
traditional volunteered geographic information. Our ambition is to include
available narrative information in an effort to better explain geospatial
relationships: with spatial reasoning being a basic form of human cognition,
narratives expressing such experiences typically contain qualitative spatial
data, i.e., spatial objects and spatial relationships.
To this end, we formulate a quantitative approach for the representation of
qualitative spatial relations extracted from UGC in the form of texts. The
proposed method quantifies such relations based on multiple text observations.
Such observations provide distance and orientation features which are utilized
by a greedy Expectation Maximization-based (EM) algorithm to infer a
probability distribution over predefined spatial relationships; the latter
represent the quantified relationships under user-defined probabilistic
assumptions. We evaluate the applicability and quality of the proposed approach
using real UGC data originating from an actual travel blog text corpus. To
verify the quality of the result, we generate grid-based maps visualizing the
spatial extent of the various relations
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
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Application of Natural Language Processing and Evidential Analysis to Web-Based Intelligence Information Acquisition
The quality of decisions made in business and government relates directly to the quality of the information used to formulate the decision. This information may be retrieved from an organization's knowledge base (Intranet) or from the World Wide Web. Intelligence services Intranet held information can be efficiently manipulated by technologies based upon either semantics such as ontologies, or statistics such as meaning-based computing. These technologies require complex processing of large amount of textual information. However, they cannot currently be effectively applied to Web-based search due to various obstacles, such as lack of semantic tagging. A new approach proposed in this paper supports Web-based search for intelligence information utilizing evidence-based natural language processing (NLP). This approach combines traditional NLP methods for filtering of Web-search results, Grounded Theory to test the completeness of the evidence, and Evidential Analysis to test the quality of gathered information. The enriched information derived from the Web-search will be transferred to the intelligence services knowledge base for handling by an effective Intranet search system thus increasing substantially the information for intelligence analysis. The paper will show that the quality of retrieved information is significantly enhanced by the discovery of previously unknown facts derived from known facts
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