311 research outputs found
A Sentence Structure-based Approach to Unsupervised Author Identification
Full text linkAssessing whether two documents were written by the same author is a crucial task, especially in the Internet age, with possible applications to philology and forensics.
The problem has been tackled in the literature by exploiting frequency-based approaches, numeric techniques or writing style analysis. Focusing on this last perspective, this paper proposes a novel technique that takes into account the structure of sentences, assuming that it is strictly related to the author's writing style. Specifically, a (collection of) text(s) in natural language written by a given author is translated into a set of First-Order Logic descriptions, and a model of the author's writing habits is obtained as the result of clustering these descriptions. Then, if an overlapping exists between the models of a known author and of an unknown one, the conclusion can be drawn that they are the same person. Among the advantages of this approach, it does not need a training phase, and performs well also on short texts and/or small collections
Unsupervised author identification and characterization
No full textAuthor identification is a hot topic, especially in the Internet age. Following our previous work in which we proposed a novel approach to this problem, based on relational representations that take into account the structure of sentences, here we present a tool that computes and visualizes a numerical and graphical characterization of the authors/texts based on several linguistic features. This tool, that extends a previous language analysis tool, is the ideal complement to the author identification technique, that is based on a clustering procedure whose outcomes (i.e., the authors’ models) are not human-readable. Both approaches are unsupervised, which allows them to tackle problems to which other state-of-the-art systems are not applicable
A Domain Based Approach to Information Retrieval in Digital Libraries
No full textThe current abundance of electronic documents requires automatic techniques that support the users in understanding their content
and extracting useful information. To this aim, improving the retrieval
performance must necessarily go beyond simple lexical interpretation of
the user queries, and pass through an understanding of their semantic
content and aims. It goes without saying that any digital library would
take enormous advantage from the availability of eective Information Retrieval techniques to provide to their users. This paper proposes an approach to Information Retrieval based on a correspondence of the domain of discourse between the query and the documents in the repository. Such
an association is based on standard general-purpose linguistic resources
(WordNet and WordNet Domains) and on a novel similarity assessment technique. Although the work is at a preliminary stage, interesting initial
results suggest to go on extending and improving the approach
Integration Strategy and Tool between Formal Ontology and Graph Database Technology
Full text linkOntologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complementarity and connections between these technologies are patent and intuitive, little exists to bring them to full integration and cooperation. This paper aims at bridging this gap, by proposing an intermediate format that can be easily mapped onto the formal ontology on one hand, so as to allow complex reasoning, and onto the graph database on the other, so as to benefit from efficient data handling
Learning to Recognize Critical Cells in Document Tables
No full textTables are among the most informative components of documents, because they are exploited to compactly and intuitively represent data, typically for understandability purposes. The needs are to identify and extract tables from documents, and, on the other hand, to be able to extract the data they contain. The latter task involves the understanding of a table structure. Due to the variability in style, size, and aims of tables, algorithmic approaches to this task can be insufficient, and the exploitation of machine learning systems may represent an effective solution. This paper proposes the exploitation of a first-order logic representation, that is able to capture the complex spatial relationships involved in a table structure, and of a learning system that can mix the power of this representation with the flexibility of statistical approaches. The obtained encouraging results suggest further investigation and refinement of the proposal
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