Semi Supervised Methods For Out Of Domain Dependency Parsing
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Semi-Supervised Dependency Parsing
| Author | : Wenliang Chen |
| Publisher | : Springer |
| Total Pages | : 149 |
| Release | : 2015-07-16 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 9812875522 |
This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with relatively small labeled data and have shown their advantages in the context of dependency parsing for many languages. Various semi-supervised dependency parsing approaches have been proposed in recent works which utilize different types of information gleaned from unlabeled data. The book offers readers a comprehensive introduction to these approaches, making it ideally suited as a textbook for advanced undergraduate and graduate students and researchers in the fields of syntactic parsing and natural language processing.
Semi-Supervised Learning and Domain Adaptation in Natural Language Processing
| Author | : Anders Søgaard |
| Publisher | : Springer Nature |
| Total Pages | : 93 |
| Release | : 2022-05-31 |
| Genre | : Computers |
| ISBN | : 3031021495 |
This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introduce what is necessary to appreciate the major challenges we face in contemporary NLP related to data sparsity and sampling bias, without wasting too much time on details about supervised learning algorithms or particular NLP applications. I use text classification, part-of-speech tagging, and dependency parsing as running examples, and limit myself to a small set of cardinal learning algorithms. I have worried less about theoretical guarantees ("this algorithm never does too badly") than about useful rules of thumb ("in this case this algorithm may perform really well"). In NLP, data is so noisy, biased, and non-stationary that few theoretical guarantees can be established and we are typically left with our gut feelings and a catalogue of crazy ideas. I hope this book will provide its readers with both. Throughout the book we include snippets of Python code and empirical evaluations, when relevant.
Dependency Parsing
| Author | : Sandra Kübler |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 128 |
| Release | : 2009 |
| Genre | : Computers |
| ISBN | : 1598295969 |
Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts
Ensembles of Diverse Clustering-based Discriminative Dependency Parsers
| Author | : Marzieh Razavi |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2012 |
| Genre | : Cluster analysis |
| ISBN | : |
Syntactic parsing and dependency parsing in particular are a core component of many Natural Language Processing (NLP) tasks and applications. Improvements in dependency parsing can help improve machine translation and information extraction applications among many others. In this thesis, we extend the framework of (Koo, Carreras, and Collins, 2008) for dependency parsing which uses a single clustering method for semi-supervised learning. We make use of multiple diverse clustering methods to build multiple discriminative dependency parsing models in the Maximum Spanning Tree (MST) parsing framework (McDonald, Crammer, and Pereira, 2005). All of these diverse clustering-based parsers are then combined together using a novel ensemble model, which performs exact inference on the shared hypothesis space of all the parser models. We show that diverse clustering-based parser models and the ensemble method together significantly improves unlabeled dependency accuracy from 90.82% to 92.46% on Section 23 of the Penn Treebank. We also show significant improvements in domain adaptation to the Switchboard and Brown corpora.
Towards Less Supervision in Dependency Parsing
| Author | : Seyedabolghasem Mirroshandel |
| Publisher | : |
| Total Pages | : 110 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
Probabilistic parsing is one of the most attractive research areas in natural language processing. Current successful probabilistic parsers require large treebanks which are difficult, time consuming, and expensive to produce. Therefore, we focused our attention on less-supervised approaches. We suggested two categories of solution: active learning and semi-supervised algorithm. Active learning strategies allow one to select the most informative samples for annotation. Most existing active learning strategies for parsing rely on selecting uncertain sentences for annotation. We show in our research, on four different languages (French, English, Persian, and Arabic), that selecting full sentences is not an optimal solution and propose a way to select only subparts of sentences. As our experiments have shown, some parts of the sentences do not contain any useful information for training a parser, and focusing on uncertain subparts of the sentences is a more effective solution in active learning.
Sentiment Analysis and Opinion Mining
| Author | : Bing Liu |
| Publisher | : Springer Nature |
| Total Pages | : 167 |
| Release | : 2022-05-31 |
| Genre | : Computers |
| ISBN | : 3031021452 |
Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography
Trends in Parsing Technology
| Author | : Harry Bunt |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 300 |
| Release | : 2010-10-06 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 9048193524 |
Computer parsing technology, which breaks down complex linguistic structures into their constituent parts, is a key research area in the automatic processing of human language. This volume is a collection of contributions from leading researchers in the field of natural language processing technology, each of whom detail their recent work which includes new techniques as well as results. The book presents an overview of the state of the art in current research into parsing technologies, focusing on three important themes: dependency parsing, domain adaptation, and deep parsing. The technology, which has a variety of practical uses, is especially concerned with the methods, tools and software that can be used to parse automatically. Applications include extracting information from free text or speech, question answering, speech recognition and comprehension, recommender systems, machine translation, and automatic summarization. New developments in the area of parsing technology are thus widely applicable, and researchers and professionals from a number of fields will find the material here required reading. As well as the other four volumes on parsing technology in this series this book has a breadth of coverage that makes it suitable both as an overview of the field for graduate students, and as a reference for established researchers in computational linguistics, artificial intelligence, computer science, language engineering, information science, and cognitive science. It will also be of interest to designers, developers, and advanced users of natural language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.
Artificial Intelligence and Security
| Author | : Xingming Sun |
| Publisher | : Springer Nature |
| Total Pages | : 695 |
| Release | : 2020-09-12 |
| Genre | : Computers |
| ISBN | : 9811581010 |
The 3-volume set CCIS 1252 until CCIS 1254 constitutes the refereed proceedings of the 6th International Conference on Artificial Intelligence and Security, ICAIS 2020, which was held in Hohhot, China, in July 2020. The conference was formerly called “International Conference on Cloud Computing and Security” with the acronym ICCCS. The total of 178 full papers and 8 short papers presented in this 3-volume proceedings was carefully reviewed and selected from 1064 submissions. The papers were organized in topical sections as follows: Part I: artificial intelligence; Part II: artificial intelligence; Internet of things; information security; Part III: information security; big data and cloud computing; information processing.
Advances in Artificial Intelligence
| Author | : Cory Butz |
| Publisher | : Springer |
| Total Pages | : 447 |
| Release | : 2011-05-25 |
| Genre | : Computers |
| ISBN | : 3642210430 |
This book constitutes the refereed proceedings of the 24th Conference on Artificial Intelligence, Canadian AI 2011, held in St. John’s, Canada, in May 2011. The 23 revised full papers presented together with 22 revised short papers and 5 papers from the graduate student symposium were carefully reviewed and selected from 81 submissions. The papers cover a broad range of topics presenting original work in all areas of artificial intelligence, either theoretical or applied.
