Alejandro Sobrino

Extraction, analysis and representation of imperfect conditional and causal sentences by means of a semi-automatic process

Causality is not only a matter of causal statements, but also of conditional sentences. In conditional statements, causality generally emerges from the entailment relationship between the antecedent and the consequence. This entailment is frequently vague and uncertain in nature. In this article, we present a method of retrieving crisp and imperfect conditional and causal sentences identified by some linguistic patterns. These sentences are pre-processed to obtain both single cause-effect structures and causal chains. The result is displayed automatically in an imperfect causal graph by means of a Java application. The causal graph shows the strenght of the causal link labelling it with a fuzzy quantifier and the intensity of the cause or effect nodes with linguistics hedges. The knowledge base used to provide automatic information based on causal relations was some medical texts, suited for the described process.

Extraction of Conditional and Causal Sentences from Queries to Provide a Flexible Answer

This paper presents a flexible retrieval method for Q/A systems based on causal knowledge. Causality is not only a matter of causal statements, but also of conditional sentences. In conditional statements, causality generally emerges from the entailment relationship between the antecedent and the consequence. In this article, we present a method of retrieving conditional and causal sentences, in particular those identified by the presence of certain interrogative particles. These sentences are pre-processed to obtain both single cause-effect structures and causal chains. The knowledge base used to provide automatic answers based on causal relations are some medical texts, adapted to the described process. Causal paths permit qualifications in terms of weighting the intensity of the cause or the strength of links connecting causes to effects. A formalism that combines degrees of truth and McCulloch-Pitts cells enables us to weight the effect with a value and thereby obtain a flexible answer.

The Role of Synonymy and Antonymy in ’Natural’ Fuzzy Prolog

The aim of this paper is to attempt a first approach to a kind of ‘natural Fuzzy Prolog’ based on the linguistic relations of synonymy and antonymy. Traditionally, Prolog was associated to the clausal logic, a disposition of the classical logic in which the goals are conjectural theorems and the answers, provided by the interpreter, are achieved using resolution and unification. Both resolution and unification are the core of a Prolog interpreter. Classical Prolog has had and still currently has interesting applications in domains as natural language processing where the problems are verbalized using crisp language and algorithmic style. But as Zadeh pointed out, natural language is essentially ill-defined or vague. Fuzzy Prolog provides tools for dealing with tasks that involve vague or imprecise statements and approximate reasoning. Traditionally, fuzzy Prolog was related with the specification of facts or rules as a matter of degree. Degrees adopted several forms: single degrees, intervals of degrees and linguistic truth-values, represented by triangular or trapezoidal numbers. Fuzzy solutions using degrees are valuable, but far from the way employed by human beings to solve daily problems. Using a naive style, this paper introduces a ‘natural fuzzy Prolog’ that deals with a kind of natural resolution applying antonymy as a linguistic negation and synonymy as a way to match predicates with similar meanings.

Imperfect Causality: Combining Experimentation and Theory

This paper is a journey around causality, imperfect causality, causal models and experiments for testing hypothesis about what causality is, with special attention to imperfect causality. Causal relations are compared with logic relations and analogies and differences are highlighted. Classical properties of causality are described and one characteristic more is added: causes, effects and the cause-effect links usually are qualified by different degrees of strength. Causal sentences automatically recovered from texts show this. In daily life, imperfect causality has an extensive role in causal decision-making. Bayes Nets offer an appropriate model to characterize causality in terms of conditional probabilities, explaining not only how choices are made but also how to learn new causal squemes based on the previously specified. Psychological experiments seem to support this view. But Bayes Nets have an Achilles hell: if the names labeling nodes are vague in meaning, the probability cannot be specified in an exact way. Fuzzy logic offers models to deals with vagueness in language. Kosko fuzzy cognitive maps provide the classical way to address fuzzy causalility. Other less relevant models to manage imperfect causality are proposed, but fuzzy people still lacks of a comprehensive batterie of examples to test those models about how fuzzy causality works. We provide a program that retrieves causal and conditional causal sentences from texts and authomatically depicts a graph representing causal concepts as well as the links between them, including fuzzy quantifiers and semantic hedges modifying nodes and links. Get these mechanisms can provide a benchmark to test hyphotesis about what is fuzzy causality, contributing to improve the current models.

Inquiry About the Origin and Abundance of Vague Language: An Issue for the Future

The origin of language has entered the current concern of the study of language as a topic of great interest and debate. Although vagueness is one of the most common features of common language, there are few references to about its roots. From a modern point of view, the main property characterizing human language is the ability to generate infinite sentences using recursion. Current linguistics emphasizes the role of the recursion in the consolidation of human language, underscoring center-embedded or coordinated sentences as the top of complexity in the generation process. But pragmatics, not only syntax, seems to play a relevant role in everyday language. Vague language, as previously said, is almost ubiquitous and present in many of the words we utter. We can hardly imagine a communication without using vague words. Thus, they are constitutive of human language as structural properties do. To inquire about how these words may have arisen in the language evolution and why they are so abundant seems to be an interesting challenge. Gossip is a kind of social communication that uses narratives to generally refer the rules that guide our behavior in the complexities of the social and cultural life. Gossip is related to vagueness as there is unthinkable gossiping using only precise meanings. Using expressions not completely defined, nonsense terms, generalities and vague language are common in gossiping. In this work we will show that many functions attributed to vague lexicon matches with many roles characteristics of gossiping. Thus, gossip seems to be a promising place to inquire the origins and abundance of vague language.

A proposal of fuzzy chained syllogism based on the concept of synonymy

Syllogistic reasoning is a type of inference pattern that involves quantified statements through the chaining of two terms by a middle term. In its classical version, only classical crisp quantifiers (all, no, some and some...not) and crisp sets in the terms are considered. If the syllogism involves vagueness, this can appear in the quantifiers (fuzzy quantifiers) or in the terms. The case of the vagueness in the quantification is analysed in Fuzzy Logic under the label of fuzzy syllogism but vagueness in the terms was not approached before now in the same depth. In this paper, we study this kind of syllogism and make a proposal for qualifying the conclusion by a degree of confidence that depends on the degree of similarity between the middle terms.

Retrieving Crisp and Imperfect Causal Sentences in Texts: From Single Causal Sentences to Mechanisms

Causality is a fundamental notion in every field of science. In empirical sciences, such as physics, causality is a typical way of generating knowledge and providing explanations. Usually, causation is a kind of relationship between two entities: cause and effect. The cause provokes an effect, and the effect is derived from the cause, so there is a relationship of strong dependence between cause and effect. Causality and conditionality are closely related. One of the main topics in the field of causality is to analyze the relationship between causality and conditionality, and to determine which causal relationships can be formulated as conditional links. In this work a method has been developed to extract causal and conditional sentences from texts belonging to different genres or disciplines, using them as a database to study imperfect causality and to explore the causal relationships of a given concept by means of a causal graph. The process is divided into three major parts. The first part creates a causal knowledge base by means of a detection and classification processes which are able to extract those sentences matching any of the causal patterns selected for this task. The second part selects those sentences related to an input concept and creates a brief summary of them, retrieving the concepts involved in the causal relationship such as the cause and effect nodes, its modifiers, linguistic edges and the type of causal relationship. The third part presents a graphical representation of the causal relationships through a causal graph, with nodes and relationships labelled with linguistic hedges that denote the intensity with which the causes or effects happen. This procedure should help to explore the role of causality in different areas such as medicine, biology, social sciences and engineering.

Mining answers for causal questions in a medical example

The aim of this paper is to approach causal questions in a medical domain. Causal questions par excellence are what, how and why-questions. The ‘pyramid of questions’ shows this. At the top, why-questions are the prototype of causal questions. Usually why-questions are related to scientific explanations. Although cover law explanation is characteristically of physical sciences, it is less common in biological or medical knowledge. In medicine, laws applied to all cases are rare. It seems that doctors express their knowledge using mechanisms instead of natural laws. In this paper we will approach causal questions with the aim of: (1) answering what-questions as identifying the cause of an effect; (2) answering how-questions as selecting an appropriate part of a mechanism that relates pairs of cause-effect (3) answering why-questions as identifying ultimate causes in the answers of how-questions. In this task, we hypothesize that why-questions are related to scientific explanations in a negative and a positive note: (i) as previously said, scientific explanations in biology are based on mechanisms instead of natural laws; (ii) scientific explanations are generally concerned with deepening, providing explanations as detailed as possible. Thus, we conjecture that answers to why-questions have to find the ultimate causes in a mechanism and link them to the prior cause summarizing the intermediate nodes in order to provide a comprehensible answer. The Mackie´s INUS causality offers a theoretical support for this solution.

Causality and imperfect causality from texts: A frame for causality in social sciences

The focus of this paper is the study of causality in both its crisp and approximate forms. Crisp causality is characterized by some properties and modalities and is related to semantic implications. This paper presents a program that extracts causal and conditional sentences with causal content from several texts. The examples extracted show that, even in scientific texts, causality can be imprecise or imperfect, as shown by the linguistic modifiers or the fuzzy quantifiers embedded in them. Quantum mechanics introduces imprecision in physics, but social sciences are the disciplines that show more circumstantial and imperfect links between cause and effect. In social sciences, there are two theoretical paradigms to understanding imperfect causality: (i) cause as an ‘ideal type’, from Weber, (ii) cause as a ‘family resemblance predicate’, from Anscombe, a follower of Wittgensteins philosophy. Our work provides two short exemplifications of these paradigms using the causal or conditional sentences retrieved from texts of different genres.

Measures and Vagueness

In this paper we will discuss some aspects concerning the application of the concept of measure to quantitative and qualitative or vague properties, highlighting the limitations caused by the high order vagueness and pointing to tuning and propagation as factors to take into account when the measure of a vague predicate is involved.