Publication
Towards Preemptive Text Edition using Topic Matching on Corpora
| Summary: | Nowadays, the results of scientific research are only recognized when published in papers for international journals or magazines of the respective area of knowledge. This perspective reflects the importance of having the work reviewed by peers. The revision encompasses a thorough analysis on the work performed, including quality of writing and whether the study advances the state-of-the-art, among other details. For these reasons, with the publishing of the document, other researchers have an assurance of the high quality of the study presented and can, therefore, make direct usage of the findings in their own work. The publishing of documents creates a cycle of information exchange responsible for speeding up the progress behind the development of new techniques, theories and technologies, resulting in added value for the entire society. Nonetheless, the existence of a detailed revision of the content sent for publication requires additional effort and dedication from its authors. They must make sure that the manuscript is of high quality, since sending a document with mistakes conveys an unprofessional image of the authors, which may result in the rejection at the journal or magazine. The objective of this work is to develop an algorithm capable of assisting in the writing of this type of documents, by proposing suggestions of possible improvements or corrections according to its specific context. The general idea for the solution proposed is for the algorithm to calculate suggestions of improvements by comparing the content of the document being written in to that of similar published documents on the field. In this context, a study on Natural Language Processing (NLP) techniques used in the creation of models for representing the document and its subjects was performed. NLP provides the tools for creating models to represent the documents and identify their topics. The main concepts include n-grams and topic modeling. The study included also an analysis of some works performed in the field of academic writing. The structure and contents of this type of documents, the presentation of some of the characteristics that are common to high quality articles, as well as the tools developed with the objective of helping in its writing were also subject of analysis. The developed algorithm derives from the combination of several tools backed up by a collection of documents, as well as the logic connecting all components, implemented in the scope of this Master’s. The collection of documents is constituted by full text of articles from different areas, including Computer Science, Physics and Mathematics, among others. The topics of these documents were extracted and stored in order to be fed to the algorithm. By comparing the topics extracted from the document under analysis with those from the documents in the collection, it is possible to select its closest documents, using them for the creation of suggestions. The algorithm is capable of proposing suggestions for word replacements which are more commonly utilized in a given field of knowledge through a set of tools used in syntactic analysis, synonyms search and morphological realization. Both objective and subjective tests were conducted on the algorithm. They demonstrate that, in some cases, the algorithm proposes suggestions which approximate the terms used in the document to the most utilized terms in the state-of-the-art of a defined scientific field. This points towards the idea that the usage of the algorithm should improve the quality of the documents, as they become more similar to the ones already published. Even though the improvements to the documents are minimal, they should be understood as a lower bound for the real utility of the algorithm. This statement is partially justified by the existence of several parsing errors both in the training and test sets, resulting from the parsing of the pdf files from the original articles, which can be improved in a production system. The main contributions of this work include the presentation of the study performed on the state of the art, the design and implementation of the algorithm and the text editor developed as a proof of concept. The analysis on the specificity of the context, which results from the tests performed on different areas of knowledge, and the large collection of documents, gathered during this Master’s program, are also important contributions of this work. |
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| Main Authors: | Correia, Acácio Filipe Pereira Pinto |
| Subject: | Artigos Documentos Científicos Language Tool Latent Dirichlet Allocation Lda N-Gram Pln Processamento de Linguagem Natural Qualidade Sugestões Wordnet. |
| Year: | 2016 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | Universidade da Beira Interior |
| Language: | English |
| Origin: | uBibliorum |
| Summary: | Nowadays, the results of scientific research are only recognized when published in papers for international journals or magazines of the respective area of knowledge. This perspective reflects the importance of having the work reviewed by peers. The revision encompasses a thorough analysis on the work performed, including quality of writing and whether the study advances the state-of-the-art, among other details. For these reasons, with the publishing of the document, other researchers have an assurance of the high quality of the study presented and can, therefore, make direct usage of the findings in their own work. The publishing of documents creates a cycle of information exchange responsible for speeding up the progress behind the development of new techniques, theories and technologies, resulting in added value for the entire society. Nonetheless, the existence of a detailed revision of the content sent for publication requires additional effort and dedication from its authors. They must make sure that the manuscript is of high quality, since sending a document with mistakes conveys an unprofessional image of the authors, which may result in the rejection at the journal or magazine. The objective of this work is to develop an algorithm capable of assisting in the writing of this type of documents, by proposing suggestions of possible improvements or corrections according to its specific context. The general idea for the solution proposed is for the algorithm to calculate suggestions of improvements by comparing the content of the document being written in to that of similar published documents on the field. In this context, a study on Natural Language Processing (NLP) techniques used in the creation of models for representing the document and its subjects was performed. NLP provides the tools for creating models to represent the documents and identify their topics. The main concepts include n-grams and topic modeling. The study included also an analysis of some works performed in the field of academic writing. The structure and contents of this type of documents, the presentation of some of the characteristics that are common to high quality articles, as well as the tools developed with the objective of helping in its writing were also subject of analysis. The developed algorithm derives from the combination of several tools backed up by a collection of documents, as well as the logic connecting all components, implemented in the scope of this Master’s. The collection of documents is constituted by full text of articles from different areas, including Computer Science, Physics and Mathematics, among others. The topics of these documents were extracted and stored in order to be fed to the algorithm. By comparing the topics extracted from the document under analysis with those from the documents in the collection, it is possible to select its closest documents, using them for the creation of suggestions. The algorithm is capable of proposing suggestions for word replacements which are more commonly utilized in a given field of knowledge through a set of tools used in syntactic analysis, synonyms search and morphological realization. Both objective and subjective tests were conducted on the algorithm. They demonstrate that, in some cases, the algorithm proposes suggestions which approximate the terms used in the document to the most utilized terms in the state-of-the-art of a defined scientific field. This points towards the idea that the usage of the algorithm should improve the quality of the documents, as they become more similar to the ones already published. Even though the improvements to the documents are minimal, they should be understood as a lower bound for the real utility of the algorithm. This statement is partially justified by the existence of several parsing errors both in the training and test sets, resulting from the parsing of the pdf files from the original articles, which can be improved in a production system. The main contributions of this work include the presentation of the study performed on the state of the art, the design and implementation of the algorithm and the text editor developed as a proof of concept. The analysis on the specificity of the context, which results from the tests performed on different areas of knowledge, and the large collection of documents, gathered during this Master’s program, are also important contributions of this work. |
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