Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review

Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, with the aim of answering the question of whether NeSy is indeed meeting its promises: reasoning, out-of-distribution generalization, interpretability, learning and reasoning from small data, and transferability to new domains. We examine the impact of knowledge representation, such as rules and semantic networks, language structure and relational structure, and whether implicit or explicit reasoning contributes to higher promise scores. We find that systems where logic is compiled into the neural network lead to the most NeSy goals being satisfied, while other factors such as knowledge representation, or type of neural architecture do not exhibit a clear correlation with goals being met. We find many discrepancies in how reasoning is defined, specifically in relation to human level reasoning, which impact decisions about model architectures and drive conclusions which are not always consistent across studies. Hence we advocate for a more methodical approach to the application of theories of human reasoning as well as the development of appropriate benchmarks, which we hope can lead to a better understanding of progress in the field. We make our data and code available on github for further analysis.Comment: Surve

Self-adaptive Based Model for Ambiguity Resolution of The Linked Data Query for Big Data Analytics

Integration of heterogeneous data sources is a crucial step in big data analytics, although it creates ambiguity issues during mapping between the sources due to the variation in the query terms, data structure and granularity conflicts. However, there are limited researches on effective big data integration to address the ambiguity issue for big data analytics. This paper introduces a self-adaptive model for big data integration by exploiting the data structure during querying in order to mitigate and resolve ambiguities. An assessment of a preliminary work on the Geography and Quran dataset is reported to illustrate the feasibility of the proposed model that motivates future work such as solving complex query

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

COOPERATIVE QUERY ANSWERING FOR APPROXIMATE ANSWERS WITH NEARNESS MEASURE IN HIERARCHICAL STRUCTURE INFORMATION SYSTEMS

Cooperative query answering for approximate answers has been utilized in various problem domains. Many challenges in manufacturing information retrieval, such as: classifying parts into families in group technology implementation, choosing the closest alternatives or substitutions for an out-of-stock part, or finding similar existing parts for rapid prototyping, could be alleviated using the concept of cooperative query answering. Most cooperative query answering techniques proposed by researchers so far concentrate on simple queries or single table information retrieval. Query relaxations in searching for approximate answers are mostly limited to attribute value substitutions. Many hierarchical structure information systems, such as manufacturing information systems, store their data in multiple tables that are connected to each other using hierarchical relationships - "aggregation", "generalization/specialization", "classification", and "category". Due to the nature of hierarchical structure information systems, information retrieval in such domains usually involves nested or jointed queries. In addition, searching for approximate answers in hierarchical structure databases not only considers attribute value substitutions, but also must take into account attribute or relation substitutions (i.e., WIDTH to DIAMETER, HOLE to GROOVE). For example, shape transformations of parts or features are possible and commonly practiced. A bar could be transformed to a rod. Such characteristics of hierarchical information systems, simple query or single-relation query relaxation techniques used in most cooperative query answering systems are not adequate. In this research, we proposed techniques for neighbor knowledge constructions, and complex query relaxations. We enhanced the original Pattern-based Knowledge Induction (PKI) and Distribution Sensitive Clustering (DISC) so that they can be used in neighbor hierarchy constructions at both tuple and attribute levels. We developed a cooperative query answering model to facilitate the approximate answer searching for complex queries. Our cooperative query answering model is comprised of algorithms for determining the causes of null answer, expanding qualified tuple set, expanding intersected tuple set, and relaxing multiple condition simultaneously. To calculate the semantic nearness between exact-match answers and approximate answers, we also proposed a nearness measuring function, called "Block Nearness", that is appropriate for the query relaxation methods proposed in this research

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Rule Generation Based On Structural Clustering For Automatic Question Answering

In rule-based methods for Question-Answering (QA) research, typical rule discovery techniques are based on structural pattern overlapping and lexical information. These usually result in rules that may require further interpretation and rules that may be redundant. To address these issues, an automatic structural rule generation algorithm is presented via clustering, where a center sentence-based clustering method is designed to automatically generate rules for QA systems

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks