The Theory And Practice Of Discourse Parsing And Summarization
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The Theory and Practice of Discourse Parsing and Summarization
Most discourse researchers assume that full semantic understanding is necessary to derive the discourse structure of texts. This book documents an attempt to construct and use automatic and non-semantic computational structures for text summarization.
Developing Enterprise Chatbots
A chatbot is expected to be capable of supporting a cohesive and coherent conversation and be knowledgeable, which makes it one of the most complex intelligent systems being designed nowadays. Designers have to learn to combine intuitive, explainable language understanding and reasoning approaches with high-performance statistical and deep learning technologies. Today, there are two popular paradigms for chatbot construction: 1. Build a bot platform with universal NLP and ML capabilities so that a bot developer for a particular enterprise, not being an expert, can populate it with training data; 2. Accumulate a huge set of training dialogue data, feed it to a deep learning network and expect the trained chatbot to automatically learn “how to chat”. Although these two approaches are reported to imitate some intelligent dialogues, both of them are unsuitable for enterprise chatbots, being unreliable and too brittle. The latter approach is based on a belief that some learning miracle will happen and a chatbot will start functioning without a thorough feature and domain engineering by an expert and interpretable dialogue management algorithms. Enterprise high-performance chatbots with extensive domain knowledge require a mix of statistical, inductive, deep machine learning and learning from the web, syntactic, semantic and discourse NLP, ontology-based reasoning and a state machine to control a dialogue. This book will provide a comprehensive source of algorithms and architectures for building chatbots for various domains based on the recent trends in computational linguistics and machine learning. The foci of this book are applications of discourse analysis in text relevant assessment, dialogue management and content generation, which help to overcome the limitations of platform-based and data driven-based approaches. Supplementary material and code is available at https://github.com/bgalitsky/relevance-based-on-parse-trees
Computational Linguistics and Intelligent Text Processing
th CICLing 2010 was the 11 Annual Conference on Intelligent Text Processing and Computational Linguistics. The CICLing conferences provide a wide-scope forum for discussion of the art and craft of natural language processing research as well as the best practices in its applications. This volume contains three invited papers and the regular papers accepted for oral presentation at the conference. The papers accepted for poster pres- tation were published in a special issue of another journal (see information on thewebsite).Since 2001,theproceedingsofCICLingconferenceshavebeen p- lished in Springer’s Lecture Notes in Computer Science series, as volumes 2004, 2276, 2588, 2945, 3406, 3878, 4394, 4919, and 5449. The volume is structured into 12 sections: – Lexical Resources – Syntax and Parsing – Word Sense Disambiguation and Named Entity Recognition – Semantics and Dialog – Humor and Emotions – Machine Translation and Multilingualism – Information Extraction – Information Retrieval – Text Categorization and Classi?cation – Plagiarism Detection – Text Summarization – Speech Generation The 2010 event received a record high number of submissions in the - year history of the CICLing series. A total of 271 papers by 565 authors from 47 countriesweresubmittedforevaluationbytheInternationalProgramCommittee (see Tables 1 and 2). This volume contains revised versions of 61 papers, by 152 authors, selected for oral presentation; the acceptance rate was 23%.