Dynamic Probabilistic Entailment. Improving on Adams' Dynamic Entailment Relation

The inferences of contraposition (A ⇒ C ∴ ¬C ⇒ ¬A), the hypothetical syllogism (A ⇒ B, B ⇒ C ∴ A ⇒ C), and others are widely seen as unacceptable for counterfactual conditionals. Adams convincingly argued, however, that these inferences are unacceptable for indicative conditionals as well. He argued that an indicative conditional of form A ⇒ C has assertability conditions instead of truth conditions, and that their assertability ‘goes with’ the conditional probability p(C|A). To account for inferences, Adams developed the notion of probabilistic entailment as an extension of classical entailment. This combined approach (correctly) predicts that contraposition and the hypothetical syllogism are invalid inferences. Perhaps less well-known, however, is that the approach also predicts that the unconditional counterparts of these inferences, e.g., modus tollens (A ⇒ C, ¬C ∴ ¬A), and iterated modus ponens (A ⇒ B, B ⇒ C, A ∴ C) are predicted to be valid. We will argue both by example and by calling to the results from a behavioral experiment (N = 159) that these latter predictions are incorrect if the unconditional premises in these inferences are seen as new information. Then we will discuss Adams’ (1998) dynamic probabilistic entailment relation, and argue that it is problematic. Finally, it will be shown how his dynamic entailment relation can be improved such that the incongruence predicted by Adams’ original system concerning conditionals and their unconditional counterparts are overcome. Finally, it will be argued that the idea behind this new notion of entailment is of more general relevance.

Regular Entailment Relations

Inspired by the work of Lorenzen on the theory of preordered groups in the forties and fifties, we define regular entailment relations and show a crucial theorem for this structure. We also describe equivariant systems of ideals à la Lorenzen and show that the remarkable regularisation process he invented yields a regular entailment relation. By providing constructive objects and arguments, we pursue Lorenzen’s aim of “bringing to light the basic, pure concepts in their simple and transparent clarity”.

Asymmetric Attributional Word Similarity Measures to Detect the Relations of Textual Generality

In this work, we present a new unsupervised and language-independent methodology to detect the relations of textual generality. For this, we introduce a particular case of Textual Entailment (TE), namely Textual Entailment by Generality (TEG). TE aims to capture primary semantic inference needs across applications in Natural Language Processing (NLP). Since 2005, in the TE Recognition (RTE) task, systems have been asked to automatically judge whether the meaning of a portion of the text, the Text (T), entails the meaning of another text, the Hypothesis (H). Several novel approaches and improvements in TE technologies demonstrated in RTE Challenges are signaling renewed interest towards a more in-depth and better understanding of the core phenomena involved in TE. In line with this direction, in this work, we focus on a particular case of entailment, entailment by generality, to detect the relations of textual generality. In text, there are different kinds of entailments, yielded from different types of implicative reasoning (lexical, syntactical, 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 general one. Therefore, we have T→GH whenever the premise T entails the hypothesis H, this also being more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, from a pair ⟨T,H⟩ having an entailment relation. To this end, we introduce an Informative Asymmetric Measure (IAM) called Simplified Asymmetric InfoSimba (AISs), which we combine with different Asymmetric Association Measures (AAM). In this work, we hypothesize about the existence of a particular mode of TE, namely TEG. Thus, the main contribution of our study is highlighting the importance of this inference mechanism. Consequently, the new annotation data seem to be a valuable resource for the community.

Asymmetric Attributional Word Similarities Measures to detect Relations of Textual Generality

In this work we present a new unsupervised and language-independent methodology to detect relations of textual generality, for this, we introduce a particular case of textual entailment (TE), namely Textual Entailment by Generality (TEG). TE aims to capture primary semantic inference needs across applications in Natural Language Processing (NLP). Since 2005, in the TE recognition (RTE) task, systems are asked to automatically judge whether the meaning of a portion of the text, the Text - T, entails the meaning of another text, the Hypothesis - H. Several novel approaches and improvements in TE technologies demonstrated in RTE Challenges are signalling of renewed interest towards a more in-depth and better understanding of the core phenomena involved in TE. In line with this direction, in this work, we focus on a particular case of entailment, entailment by generality, to detect relations of textual generality. In-text, there are different kinds of entailment, yielded from different types of implicative reasoning (lexical, syntactical, 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 general one. Therefore, we have T→GH whenever the premise T entails the hypothesis H, being it also more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, from a pair ⟨T,H⟩ having an entailment relation. To this end, we introduce an Informative Asymmetric Measure (IAM) called Simplified Asymmetric InfoSimba (AISs), which we combine with different Asymmetric Association Measures (AAM). In this work, we hypothesize the existence of a particular mode of TE, namely TEG. Thus, the main contribution of our study is to highlight the importance of this inference mechanism. Consequently, the new annotation data seems to be a valuable resource for the community.

Rethinking nudge: not one but three concepts

Abstract‘Nudge’ is a concept of policy intervention that originates in Thaler and Sunstein's (2008) popular eponymous book. Following their own hints, we distinguish three properties of nudge interventions: they redirect individual choices by only slightly altering choice conditions (here ‘nudge 1’); they use rationality failures instrumentally (here ‘nudge 2’); and they alleviate the unfavourable effects of these failures (here ‘nudge 3’). We explore each property in semantic detail and show that no entailment relation holds between them. This calls into question the theoretical unity of nudge as intended by Thaler and Sunstein and most of their followers. We eventually recommend pursuing each property separately, both in policy research and at the foundational level. We particularly emphasise the need for reconsidering the respective roles of decision theory and behavioural economics to delineate nudge 2 correctly. The paper differs from most in the literature in focusing on the definitional rather than the normative problems of nudge.

Defeasible Inheritance-Based Description Logics

Defeasible inheritance networks are a non-monotonic framework that deals with hierarchical knowledge. On the other hand, rational closure is acknowledged as a landmark of the preferential approach to non-monotonic reasoning. We will combine these two approaches and define a new non-monotonic closure operation for propositional knowledge bases that combines the advantages of both. Then we redefine such a procedure for Description Logics (DLs), a family of logics well-suited to model structured information. In both cases we will provide a simple reasoning method that is built on top of the classical entailment relation and, thus, is amenable of an implementation based on existing reasoners. Eventually, we evaluate our approach on well-known landmark test examples.

Unsupervised acquisition of entailment relations from the Web

Entailment recognition is a primary generic task in natural language inference, whose focus is to detect whether the meaning of one expression can be inferred from the meaning of the other. Accordingly, many NLP applications would benefit from high coverage knowledgebases of paraphrases and entailment rules. To this end, learning such knowledgebases from the Web is especially appealing due to its huge size as well as its highly heterogeneous content, allowing for a more scalable rule extraction of various domains. However, the scalability of state-of-the-art entailment rule acquisition approaches from the Web is still limited. We present a fully unsupervised learning algorithm for Web-based extraction of entailment relations. We focus on increased scalability and generality with respect to prior work, with the potential of a large-scale Web-based knowledgebase. Our algorithm takes as its input a lexical–syntactic template and searches the Web for syntactic templates that participate in an entailment relation with the input template. Experiments show promising results, achieving performance similar to a state-of-the-art unsupervised algorithm, operating over an offline corpus, but with the benefit of learning rules for different domains with no additional effort.

Proto-Entailment in RS logic

In this paper I propose a formalization of proto-entailment relation introduced by V. Shalak by means of RS logic. The first section clarifies the idea and formal developments of RS logic, which is the logic of Rational Subject. In the second section I will very briefly introduce the conception of proto-entailment as it was promoted in Shalak’s writings. The third section contains the formal account for proto-entailment and axiotimatization of resulting logic.