Automatic multi-label subject indexing in a multilingual environment

This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines(SVM). The aim was to test the applicability of SVMs with a real world dataset. We have also explored the feasibility of incorporating multilingual background knowledge, as represented in thesauri or ontologies, into our text document representation for indexing purposes. The test set for our evaluations has been compiled from an extensive document base maintained by the Food and Agriculture Organization (FAO) of the United Nations (UN). Empirical results show that SVMs are a good method for automatic multi- label classification of documents in multiple languages

Human-competitive automatic topic indexing

Topic indexing is the task of identifying the main topics covered by a document. These are useful for many purposes: as subject headings in libraries, as keywords in academic publications and as tags on the web. Knowing a document's topics helps people judge its relevance quickly. However, assigning topics manually is labor intensive. This thesis shows how to generate them automatically in a way that competes with human performance. Three kinds of indexing are investigated: term assignment, a task commonly performed by librarians, who select topics from a controlled vocabulary; tagging, a popular activity of web users, who choose topics freely; and a new method of keyphrase extraction, where topics are equated to Wikipedia article names. A general two-stage algorithm is introduced that first selects candidate topics and then ranks them by significance based on their properties. These properties draw on statistical, semantic, domain-specific and encyclopedic knowledge. They are combined using a machine learning algorithm that models human indexing behavior from examples. This approach is evaluated by comparing automatically generated topics to those assigned by professional indexers, and by amateurs. We claim that the algorithm is human-competitive because it chooses topics that are as consistent with those assigned by humans as their topics are with each other. The approach is generalizable, requires little training data and applies across different domains and languages

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

Ensemble Methods in Environmental Data Mining

Environmental data mining is the nontrivial process of identifying valid, novel, and potentially useful patterns in data from environmental sciences. This chapter proposes ensemble methods in environmental data mining that combines the outputs from multiple classification models to obtain better results than the outputs that could be obtained by an individual model. The study presented in this chapter focuses on several ensemble strategies in addition to the standard single classifiers such as decision tree, naive Bayes, support vector machine, and k-nearest neighbor (KNN), popularly used in literature. This is the first study that compares four ensemble strategies for environmental data mining: (i) bagging, (ii) bagging combined with random feature subset selection (the random forest algorithm), (iii) boosting (the AdaBoost algorithm), and (iv) voting of different algorithms. In the experimental studies, ensemble methods are tested on different real-world environmental datasets in various subjects such as air, ecology, rainfall, and soil

Resolving pronominal anaphora using commonsense knowledge

Coreference resolution is the task of resolving all expressions in a text that refer to the same entity. Such expressions are often used in writing and speech as shortcuts to avoid repetition. The most frequent form of coreference is the anaphor. To resolve anaphora not only grammatical and syntactical strategies are required, but also semantic approaches should be taken into consideration. This dissertation presents a framework for automatically resolving pronominal anaphora by integrating recent findings from the field of linguistics with new semantic features. Commonsense knowledge is the routine knowledge people have of the everyday world. Because such knowledge is widely used it is frequently omitted from social communications such as texts. It is understandable that without this knowledge computers will have difficulty making sense of textual information. In this dissertation a new set of computational and linguistic features are used in a supervised learning approach to resolve the pronominal anaphora in document. Commonsense knowledge sources such as ConceptNet and WordNet are used and similarity measures are extracted to uncover the elaborative information embedded in the words that can help in the process of anaphora resolution. The anaphoric system is tested on 350 Wall Street Journal articles from the BBN corpus. When compared with other systems available such as BART (Versley et al. 2008) and Charniak and Elsner 2009, our system performed better and also resolved a much wider range of anaphora. We were able to achieve a 92% F-measure on the BBN corpus and an average of 85% F-measure when tested on other genres of documents such as children stories and short stories selected from the web