On the Inferential Zigzag and Its Activation Towards Clarifying What It Is Commonsense Reasoning

This paper has a twofold goal: The first is to study how the inferential zigzag can be activated, even computationally, trying to analyse what kind of reasoning consists of, where its ’mechanism’ is rooted, how it can be activated since without all this it can just seem a metaphysical idea. The second, not so deeply different - as it can be presumed at a first view - but complementary, is to explore the subject’s link with the old thought on conjectures of the 15th Century Theologist and Philosopher Nicolaus Cusanus who was the first thinker consciously and extensively using conjectures

‎On the Inferential Zigzag and Its Activation Towards Clarifying What It Is Commonsense Reasoning

This paper has a twofold goal‎: ‎The first is to study how the inferential zigzag can be activated‎, ‎even computationally‎, ‎trying to analyse what kind of reasoning consists of‎, ‎where its 'mechanism' is rooted‎, ‎how it can be activated since without all this it can just seem a metaphysical idea‎. ‎The second‎, ‎not so deeply different‎ - ‎as it can be presumed at a first view‎ - ‎but complementary‎, ‎is to explore the subject's link with the old thought on conjectures of the 15th Century Theologist and Philosopher Nicolaus Cusanus‎ ‎who was the first thinker consciously and extensively using conjectures‎

A Neuro Symbolic Approach for Contradiction Detection in Persian Text

Detection of semantic contradictory sentences is a challenging and fundamental issue for some NLP applications, such as textual entailments recognition. In this study, contradiction means different types of semantic confrontation, such as negation, antonymy, and numerical. Due to the lack of sufficient data to apply precise machine learning and, specifically, deep learning methods to Persian and other low-resource languages, rule-based approaches are of great interest. Also, recently, the emergence of new methods such as transfer learning has opened up the possibility of deep learning for low-resource languages. This paper introduces a hybrid contradiction detection approach for detecting seven categories of contradictions in Persian texts: Antonymy, negation, numerical, factive, structural, lexical and world knowledge. The proposed method consists of 1) a novel data mining method and 2) a transformer-based deep neural method for contradiction detection . Also, a simple baseline is presented for comparison. The data mining method uses frequent rule mining to extract appropriate contradiction detection rules employing a development set. Extracted rules are tested for different categories of contradictory sentences. In the first step, a classifier checks whether the rules work for an input sentence pair. Then, according to the result, rules are used for three categories of negation, numerical, and antonym. In this part, the highest F-measure is obtained for detecting the negation category (90%), the average F-measure for these three categories is 86%, and for the other four categories, in which the rules have a lower F-measure of 62%, the transformer-based method achieved 76%. The proposed hybrid approach has an overall f-measure of higher than 80%.&nbsp

SemNet: the knowledge representation of lolita

Many systems of Knowledge Representation exist, but none were designed specifically for general purpose large scale natural language processing. This thesis introduces a set of metrics to evaluate the suitability of representations for this purpose, derived from an analysis of the problems such processing introduces. These metrics address three broad categories of question: Is the representation sufficiently expressive to perform its task? What implications has its design on the architecture of the system using it? What inefficiencies are intrinsic to its design? An evaluation of existing Knowledge Representation systems reveals that none of them satisfies the needs of general purpose large scale natural language processing. To remedy this lack, this thesis develops a new representation: SemNet. SemNet benefits not only from the detailed requirements analysis but also from insights gained from its use as the core representation of the large scale general purpose system LOLITA (Large-scale Object-based Linguistic Interactor, Translator, and Analyser). The mapping process between Natural language and representation is presented in detail, showing that the representation achieves its goals in practice

Improving User Experience In Information Retrieval Using Semantic Web And Other Technologies

The need to find, access and extract information has been the motivation for many different fields of research in the past few years. The fields such as Machine Learning, Question Answering Systems, Semantic Web, etc. each tries to cover parts of the mentioned problem. Each of these fields have introduced many different tools and approaches which in many cases are multi-disciplinary, covering more than one of these fields to provide solution for one or more of them. On the other hand, the expansion of the Web with Web 2.0, gave researchers many new tools to extend approaches to help users extract and find information faster and easier. Currently, the size of e-commerce and online shopping, the extended use of search engines for different purposes and the amount of collaboration for creating content on the Web provides us with different possibilities and challenges which we address some of them here