[PDF] Improving Dependency Parsing Using Word Clusters eBook

Author : Jostein Lien
Publisher : LAP Lambert Academic Publishing
Page : 120 pages
File Size : 10,63 MB
Release : 2015-10-22
Category :
ISBN : 9783659794551

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Several studies have attempted to improve the accuracy in dependency parsing by including information about word clusters into the parsing models. The use of word clusters are typically motivated by the shortage of labeled training data and domain adaption, attempting to influence a parsing model for use on data from a new domain. This book shows the effect of using cluster-based features in MaltParser, a data-driven parser for inductive dependency parsing. Different clustering features are used for generating clusters, using the K-means clustering algorithm. The clusters are used as a source of additional information in an expanded feature model used by the MaltParser system. Parsing experiments are performed on several different data sets, including the Wall Street Journal and texts from various web domains. Significantly improved parsing results are reported when using a cluster-informed parser compared to the baseline parser. The contents of this book might be of interest to anyone interested in the application of machine learning in language technology.

Author : Wenliang Chen
Publisher : Springer
Page : 149 pages
File Size : 41,94 MB
Release : 2015-07-16
Category : Language Arts & Disciplines
ISBN : 9812875522

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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.

Author : Marzieh Razavi
Publisher :
Page : 116 pages
File Size : 22,71 MB
Release : 2012
Category : Cluster analysis
ISBN :

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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.

Author : Akira Hirose
Publisher : Springer
Page : 646 pages
File Size : 14,48 MB
Release : 2016-09-30
Category : Computers
ISBN : 3319466879

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The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitutes the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms.

Author : Chin-Yew Lin
Publisher : Springer
Page : 963 pages
File Size : 28,30 MB
Release : 2016-11-30
Category : Computers
ISBN : 3319504967

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This book constitutes the joint refereed proceedings of the 5th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2016, and the 24th International Conference on Computer Processing of Oriental Languages, ICCPOL 2016, held in Kunming, China, in December 2016. The 48 revised full papers presented together with 41 short papers were carefully reviewed and selected from 216 submissions. The papers cover fundamental research in language computing, multi-lingual access, web mining/text mining, machine learning for NLP, knowledge graph, NLP for social network, as well as applications in language computing.

Author : Harry Bunt
Publisher : Springer Science & Business Media
Page : 300 pages
File Size : 44,26 MB
Release : 2010-10-06
Category : Language Arts & Disciplines
ISBN : 9048193524

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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.

Author : Juanzi Li
Publisher : Springer
Page : 612 pages
File Size : 11,38 MB
Release : 2015-10-07
Category : Computers
ISBN : 3319252070

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This book constitutes the refereed proceedings of the 4th CCF Conference, NLPCC 2015, held in Nanchang, China, in October 2015. The 35 revised full papers presented together with 22 short papers were carefully reviewed and selected from 238 submissions. The papers are organized in topical sections on fundamentals on language computing; applications on language computing; NLP for search technology and ads; web mining; knowledge acquisition and information extraction.

Author : Katarzyna Tarnowska
Publisher : Springer
Page : 124 pages
File Size : 22,2 MB
Release : 2019-03-19
Category : Technology & Engineering
ISBN : 3030134385

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This book presents the Recommender System for Improving Customer Loyalty. New and innovative products have begun appearing from a wide variety of countries, which has increased the need to improve the customer experience. When a customer spends hundreds of thousands of dollars on a piece of equipment, keeping it running efficiently is critical to achieving the desired return on investment. Moreover, managers have discovered that delivering a better customer experience pays off in a number of ways. A study of publicly traded companies conducted by Watermark Consulting found that from 2007 to 2013, companies with a better customer service generated a total return to shareholders that was 26 points higher than the S&P 500. This is only one of many studies that illustrate the measurable value of providing a better service experience. The Recommender System presented here addresses several important issues. (1) It provides a decision framework to help managers determine which actions are likely to have the greatest impact on the Net Promoter Score. (2) The results are based on multiple clients. The data mining techniques employed in the Recommender System allow users to “learn” from the experiences of others, without sharing proprietary information. This dramatically enhances the power of the system. (3) It supplements traditional text mining options. Text mining can be used to identify the frequency with which topics are mentioned, and the sentiment associated with a given topic. The Recommender System allows users to view specific, anonymous comments associated with actual customers. Studying these comments can provide highly accurate insights into the steps that can be taken to improve the customer experience. (4) Lastly, the system provides a sensitivity analysis feature. In some cases, certain actions can be more easily implemented than others. The Recommender System allows managers to “weigh” these actions and determine which ones would have a greater impact.

Author : Jakub Waszczuk
Publisher : Language Science Press
Page : 326 pages
File Size : 22,32 MB
Release : 2019
Category : Language Arts & Disciplines
ISBN : 3961101450

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This book consists of contributions related to the definition, representation and parsing of MWEs. These reflect current trends in the representation and processing of MWEs. They cover various categories of MWEs such as verbal, adverbial and nominal MWEs, various linguistic frameworks (e.g. tree-based and unification-based grammars), various languages including English, French, Modern Greek, Hebrew, Norwegian), and various applications (namely MWE detection, parsing, automatic translation) using both symbolic and statistical approaches.