46,637 research outputs found
Extracting Formal Models from Normative Texts
We are concerned with the analysis of normative texts - documents based on
the deontic notions of obligation, permission, and prohibition. Our goal is to
make queries about these notions and verify that a text satisfies certain
properties concerning causality of actions and timing constraints. This
requires taking the original text and building a representation (model) of it
in a formal language, in our case the C-O Diagram formalism. We present an
experimental, semi-automatic aid that helps to bridge the gap between a
normative text in natural language and its C-O Diagram representation. Our
approach consists of using dependency structures obtained from the
state-of-the-art Stanford Parser, and applying our own rules and heuristics in
order to extract the relevant components. The result is a tabular data
structure where each sentence is split into suitable fields, which can then be
converted into a C-O Diagram. The process is not fully automatic however, and
some post-editing is generally required of the user. We apply our tool and
perform experiments on documents from different domains, and report an initial
evaluation of the accuracy and feasibility of our approach.Comment: Extended version of conference paper at the 21st International
Conference on Applications of Natural Language to Information Systems (NLDB
2016). arXiv admin note: substantial text overlap with arXiv:1607.0148
Description of the Chinese-to-Spanish rule-based machine translation system developed with a hybrid combination of human annotation and statistical techniques
Two of the most popular Machine Translation (MT) paradigms are rule based (RBMT) and corpus based, which include the statistical systems (SMT). When scarce parallel corpus is available, RBMT becomes particularly attractive. This is the case of the Chinese--Spanish language pair.
This article presents the first RBMT system for Chinese to Spanish. We describe a hybrid method for constructing this system taking advantage of available resources such as parallel corpora that are used to extract dictionaries and lexical and structural transfer rules.
The final system is freely available online and open source. Although performance lags behind standard SMT systems for an in-domain test set, the results show that the RBMTâs coverage is competitive and it outperforms the SMT system in an out-of-domain test set. This RBMT system is available to the general public, it can be further enhanced, and it opens up the possibility of creating future hybrid MT systems.Peer ReviewedPostprint (author's final draft
Knowledge Discovery in Online Repositories: A Text Mining Approach
Before the advent of the Internet, the newspapers were the prominent instrument of
mobilization for independence and political struggles. Since independence in Nigeria, the
political class has adopted newspapers as a medium of Political Competition and
Communication. Consequently, most political information exists in unstructured form and
hence the need to tap into it using text mining algorithm.
This paper implements a text mining algorithm on some unstructured data format in some newspapers. The algorithm involves the following natural language processing techniques: tokenization, text filtering and refinement. As a follow-up to the natural language techniques, association rule mining technique of data mining is used to extract knowledge using the Modified Generating Association Rules based on Weighting scheme (GARW).
The main contributions of the technique are that it integrates information retrieval scheme (Term Frequency Inverse Document Frequency) (for keyword/feature selection that automatically selects the most discriminative keywords for use in association rules generation) with Data Mining technique for association rules discovery. The program is applied to Pre-Election information gotten from the website of the Nigerian Guardian newspaper. The extracted association rules contained important features and described the informative news included in the documents collection when related to the concluded 2007 presidential election. The system presented useful information that could help sanitize the polity as well as protect the nascent democracy
RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS
In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions
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