Linguistic studies in under-resourced languages pose additional challenges at various levels, including the automatic collection of examples, cases, and corpora construction. Several sophisticated applications, such as GATE (Cunningham, 2002), can be configured/adjusted/programmed by experts to automatically collect examples from the Web in any language. However, these applications are too complex and intricate to be operated, requiring, in some cases, skills in computer science. In this work, we present TELP, a tool that allows for the simplified expression of linguistic patterns to extract case studies automatically from World Wide Web sites. It is a straightforward application with an intuitive GUI and a quick learning curve, facilitating its broad use by researchers from different domains. In this paper, we describe the operational and technical aspects of TELP and some relatively recent and relevant use cases in the field of linguistic studies.

In this paper we focus on a particular case of entailment, namely entailment by generality. We argue that there exist various types of implication, a range of different levels of entailment reasoning, based on lexical, syntactic, logical and common sense clues, at different levels of difficulty. We introduce the paradigm of Textual Entailment (TE) by Generality, which can be defined as the entailment from a specific statement towards a relatively more general statement. In this context, the Text T entails the Hypothesis H, and at the same time H is more general than T . We propose an unsupervised and language-independent method to recognize TE by Generality given a case of Text − Hypothesis or T − H where entailment relation holds.

In this work we introduce a particular case of textual entailment (TE), namely Textual Entailment by Generality (TEG). In text, there are different kinds of entailment yielded from different types of implicative reasoning (lexical, syntactic, common sense based), but here we focus just on TEG, which can be defined as an entailment from a specific statement towards a relatively more G general one. Therefore, we have T (G)→ H whenever the premise T entails the hypothesis H, the hypothesis being more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, given a pair T, H in an entailment relation. We have evaluated our proposal G → H English pairs, where we know through two experiments: (a) Test on T (G)→ H English pairs, where we know that TEG holds; (b) Test on T → H Portuguese pairs, randomly selected with 60% of TEGs and 40% of TE without generality dependency (TEnG).