Implicit learning of recursive context-free grammars

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning

The Anatomy of Knowledge: Quantitative and Qualitative Analysis of the Evolution of Ideas in Space Syntax Conference Articles (1997-2017)

Since its inception in the 1970s, space syntax has matured into a theory and a method comprising a set of recurring theoretical and analytical concepts, as well as new ones emerging through the years. How can we trace the evolution of the field through language? How can we analyse the development of ideas in space syntax research? What can we learn from this evolution about knowledge creation in this area? Recognising that language is central to the development of ideas in any field, this paper uses automated text-analyses, focusing more specifically on all papers published in the space syntax symposia proceedings from 1997 to 2017. The purpose is to trace the trajectory of ideas as they were elaborated, used and perhaps changed in the collective work of authors researching within this field in different parts of the world. Firstly, we identify concepts and technical terminology in the field through a combined quantitative and qualitative text analysis. Secondly, we statistically assess the use of these terms, revealing patterns and trends in the evolution of knowledge in space syntax. Thirdly, we compare patterns between established concepts and categories that stabilise over time with concepts emerging more recently. The results from our analysis of networks of concept relationships suggest that: (i) concepts and terms evolve in dependent trajectories; (ii) ideas have evolutionary developments, with some emerging and gaining growing attention, while others showing clear signs of stability, and others losing centrality over time, including networks of what can be termed as ‘canonical’ concepts. We have also identified (iii) an overall decline in the use of early space syntax concepts rooted in social theory and anthropology; (iv) a trend of decreasing conceptual novelty over time; (v) traces of increasing influence by other fields; and finally (vi) signs of a clear ‘technological turn’ in the field

Study And Modeling of Question Answer System Using Deep Learning Technique of AI

In this paper, the different QA system types, the theoretical foundation for deep learning models, the metaheuristic optimization techniques, and the performance assessment metrics are discussed. A suggested architecture for a question-and-answer system that takes a deep learning approach is shown here. The study also covers the constraints and factors to take into account regarding the aforementioned system

A Comparative Study of Text Summarization on E-mail Data Using Unsupervised Learning Approaches

Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study in text processing, especially for the task of summarization. This research employs a quantitative and inductive methodologies to implement the Unsupervised learning models that addresses summarization task problem, to efficiently generate more precise summaries and to determine which approach of implementing Unsupervised clustering models outperform the best. The precision score from ROUGE-N metrics is used as the evaluation metrics in this research. This research evaluates the performance in terms of the precision score of four different approaches of text summarization by using various combinations of feature embedding technique like Word2Vec /BERT model and hybrid/conventional clustering algorithms. The results reveals that both the approaches of using Word2Vec and BERT feature embedding along with hybrid PHA-ClusteringGain k-Means algorithm achieved increase in the precision when compared with the conventional k-means clustering model. Among those hybrid approaches performed, the one using Word2Vec as feature embedding method attained 55.73% as maximum precision value

State of the field: digital history

Computing and the use of digital sources and resources is an everyday and essential practice in current academic scholarship. The present article gives a concise overview of approaches and methods within digital historical scholarship, focussing on the question: How have the Digital Humanities evolved and what has that evolution brought to historical scholarship? We begin by discussing techniques in which data are generated and machine searchable, such as OCR/HTR, born-digital archives, computer vision, scholarly editions, and Linked Data. In the second section, we provide examples of how data is made more accessible through quantitative text and network analysis. We close with a section on the need for hermeneutics and data-awareness in digital historical scholarship. The technologies described in this article have had varying degrees of effect on historical scholarship, usually in indirect ways. For example, technologies such as OCR and search engines may not be directly visible in a historical argument; however, these technologies do shape how historians interact with sources and whether sources can be accessed at all. It is with this article that we aim to start to take stock of the digital approaches and methods used in historical scholarship which may serve as starting points for scholars to understand the digital turn in the field and how and when to implement such approaches in their work