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Linguistic Evidence
The renaissance of corpus linguistics and promising developments in experimental linguistic techniques in recent years have led to a remarkable revival of interest in issues of the empirical base of linguistic theory in general, and the status of different kinds of linguistic evidence in particular. Consensus is growing (a) that even so-called primary data (from introspection as well as authentic language production) are inherently complex performance data only indirectly reflecting the subject of linguistic theory, (b) that for an appropriate foundation of linguistic theories evidence from different sources such as introspective data, corpus data, data from (psycho-)linguistic experiments, ...
Treebanks
This book provides a state of the art on work being done with parsed corpora. It gathers 21 papers on building and using parsed corpora raising many relevant questions, and deals with a variety of languages and a variety of corpora. It is for those working in linguistics, computational linguistics, natural language, syntax, and grammar.
Natural Language Data Management and Interfaces
The volume of natural language text data has been rapidly increasing over the past two decades, due to factors such as the growth of the Web, the low cost associated with publishing, and the progress on the digitization of printed texts. This growth combined with the proliferation of natural language systems for search and retrieving information provides tremendous opportunities for studying some of the areas where database systems and natural language processing systems overlap. This book explores two interrelated and important areas of overlap: (1) managing natural language data and (2) developing natural language interfaces to databases. It presents relevant concepts and research question...
Natural Language Processing
This textbook presents an up-to-date and comprehensive overview of Natural Language Processing (NLP), from basic concepts to core algorithms and key applications. Further, it contains seven step-by-step NLP workshops (total length: 14 hours) offering hands-on practice with essential Python tools like NLTK, spaCy, TensorFlow Kera, Transformer and BERT. The objective of this book is to provide readers with a fundamental grasp of NLP and its core technologies, and to enable them to build their own NLP applications (e.g. Chatbot systems) using Python-based NLP tools. It is both a textbook and NLP tool-book intended for the following readers: undergraduate students from various disciplines who want to learn NLP; lecturers and tutors who want to teach courses or tutorials for undergraduate/graduate students on NLP and related AI topics; and readers with various backgrounds who want to learn NLP, and more importantly, to build workable NLP applications after completing its 14 hours of Python-based workshops.
Machine Learning Challenges
This book constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
Language and Memory
This volume presents an entirely new, expanded cognitive view of language by examining linguistic structure and its use in communication from the point of view of memory, thus providing a novel way of analysing language. The fourteen chapters, authored by linguists and psychologists, show the need for such an approach and illustrate that the properties of numerous linguistic structures reflect those of memory in various ways. Many different methodologies are presented because of the interdisciplinary nature of the volume, without reducing the comprehensibility and comparability of the contributions. Core linguistic areas are discussed in the contributions embracing syntax, semantics, pragmat...
Proceedings of the Twenty-second Annual Conference of the Cognitive Science Society
Vol inclu all ppers & postrs presntd at 2000 Cog Sci mtg & summaries of symposia & invitd addresses. Dealg wth issues of representg & modelg cog procsses, appeals to scholars in all subdiscip tht comprise cog sci: psy, compu sci, neuro sci, ling, & philo
Computational Linguistics and Intelligent Text Processing
This book constitutes the refereed proceedings of the 7th International Conference on Computational Linguistics and Intelligent Text Processing, held in February 2006. The 43 revised full papers and 16 revised short papers presented together with three invited papers were carefully reviewed and selected from 176 submissions. The papers are structured into two parts and organized in topical sections on computational linguistics research.
Scientific and Statistical Database Management
This book constitutes the refereed proceedings of the 20th International Conference on Scientific and Statistical Database Management, SSDBM 2008, held in Hong Kong, China, in July 2008. The 28 revised full papers, 7 revised short papers and 8 poster and demo papers presented together with 3 invited talks were carefully reviewed and selected from 84 submissions. The papers are organized in topical sections on query optimization in scientific databases, privacy, searching and mining graphs, data streams, scientific database applications, advanced indexing methods, data mining, as well as advanced queries and uncertain data.
Knowledge Graphs
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced ...
