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Semi-Supervised Learning and Domain Adaptation in Natural Language Processing

This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance;

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Introduction to Semi-Supervised Learning

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the;

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised;

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Machine Learning and Knowledge Discovery in Databases. Research Track

: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised;

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Semi-Supervised Dependency Parsing

data and have shown their advantages in the context of dependency parsing for many languages. Various semi-supervised dependency parsing;

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Graph-Based Semi-Supervised Learning

this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed;

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Linguistic Structure Prediction

basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to;

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Machine Learning with PySpark

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;

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Linguistic Structure Prediction

basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to;

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Supervised and Unsupervised Learning for Data Science

in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Machine Learning in Complex Networks

in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a;

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Python Natural Language Processing

Learning and Deep Learning techniques for efficient natural language processing * Get started with NLTK and implement NLP in your applications;

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Domain Adaptation in Computer Vision with Deep Learning

and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second;

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Machine Learning for Text

intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning;

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Introduction to Natural Language Processing

representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language;

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Medical Image Computing and Computer Assisted Intervention - MICCAI 2021

learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning;

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Deep Learning for Natural Language Processing

various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural;

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Natural Language Processing

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops;

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

word segmentation; open-domain question answering; discourse, coreference and pragmatics; statistical and machine learning methods in NLP;

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Natural Language Processing Recipes

preprocessing, POS tagging, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language;

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Practical Natural Language Processing with Python

domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case;

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Handbook on Neural Information Processing

Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple;

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Natural Language Processing with PyTorchlow

Amazon Alexa and Google Translate possible. If you're a developer or data scientist new to NLP and deep learning, this practical guide shows you;

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Natural Language Processing

based on natural language processing; computers and language learning. Section 4 focuses on natural language in mobile systems, describing;

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Machine Learning Foundations

core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and;

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Semisupervised Learning for Computational Linguistics

material on supervised and unsupervised learning. The book presents a brief history of semisupervised learning and its place in the spectrum;

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