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The increasing scale and pace of the production of digital documents have generated a need for automatic tools to analyze documents and extract underlying concepts and knowledge in order to help humans manage information overload. Specifically, since most information comes in the form of text, natural language processing tools are needed that are able to analyze the sentences and transform them into an internal representation that can be handled by computers to perform inferences and reasoning. In turn, these tools often work based on linguistic resources for the various levels of analysis (morphological, lexical, syntactic and semantic). The resources are language (and sometimes even domain) specific and typically must be manually produced by human experts, increasing their cost and limiting their availability. Especially relevant are concept taxonomies, which allow us to properly interpret the textual content of documents. This paper presents an intelligent module to extract relevant domain knowledge from free text by means of Concept Hierarchy Extraction techniques. In particular, the underlying model is provided using Formal Concept Analysis, while a crucial role is played by an expert system for language analysis that can recognize different types of indirect objects (a component very rich in information) in English. [ABSTRACT FROM AUTHOR]

Financial analysts constitute an important element of financial decision-making in stock exchanges throughout the world. By leveraging on argumentative reasoning, we develop a method to predict financial analysts' recommendations in earnings conference calls (ECCs), an important type of financial communication. We elaborate an analysis to select those reliable arguments in the Questions & Answers (Q&A) part of ECCs that analysts evaluate to estimate their recommendation. The observation date of stock recommendation update may variate during the next quarter: it can be either the day after the ECC or it can take weeks. Our objective is to anticipate analysts' recommendations by predicting their judgment with the help of abstract argumentation. In this paper, we devise our approach to the analysis of ECCs, by designing a general processing framework which combines natural language processing along with abstract argumentation evaluation techniques to produce a final scoring function, representing the analysts' prediction about the company's trend. Then, we evaluate the performance of our approach by specifying a strategy to predict analysts recommendations starting from the evaluation of the argumentation graph properly instantiated from an ECC transcript. We also provide the experimental setting in which we perform the predictions of recommendations as a machine learning classification task. The method is shown to outperform approaches based only on sentiment analysis. [ABSTRACT FROM AUTHOR]

While multimedia digital documents are progressively spreading, most of the content of Digital Libraries is still in the form of text, and this predominance will probably never be questioned. Except pure display of these documents, all other tasks are based on some kind of Natural Language Processing, that must be supported by suitable linguistic resources. Since these resources are clearly language-specific, they might be unavailable for several languages, and manually building them is costly, time-consuming and error-prone. This paper proposes a methodology to automatically learn linguistic resources for a natural language starting from texts written in that language. The learned resources may enable further high-level processing of documents in that language, and/or be taken as a basis for further manual refinements. Experimental results show that its application may effectively provide useful linguistic resources in a fully automatic manner. [ABSTRACT FROM AUTHOR]

Tools for Natural Language Processing work using linguistic resources, that are language-specific. The complexity of building such resources causes many languages to lack them. So, learning them automatically from sample texts would be a desirable solution. This usually requires huge training corpora, which are not available for many local languages and jargons, lacking a wide literature. This paper focuses on stopwords, i.e., terms in a text which do not contribute in conveying its topic or content. It provides two main, inter-related and complementary, methodological contributions: (i) it proposes a novel approach based on term and document frequency to rank candidate stopwords, that works also on very small corpora (even single documents); and (ii) it proposes an automatic cutoff strategy to select the best candidates in the ranking, thus addressing one of the most critical problems in the stopword identification practice. Nice features of these approaches are that (i) they are generic and applicable to different languages, (ii) they are fully automatic, and (iii) they do not require any previous linguistic knowledge. Extensive experiments show that both are extremely effective and reliable. The former outperforms all comparable approaches in the state-of-the-art, both in terms of performance (Precision stays at 100% or nearly so for a large portion of the top-ranked candidate stopwords, while Recall is quite close to the maximum reachable in theory.) and in smooth behavior (Precision is monotonically decreasing, and Recall is monotonically increasing, allowing the experimenter to choose the preferred balance.). The latter is more flexible than existing solutions in the literature, requiring just one parameter intuitively related to the balance between Precision and Recall one wishes to obtain. [ABSTRACT FROM AUTHOR]