Deep Learning on Improved Word Embedding Model for Topic Classification
نویسندگان
چکیده
منابع مشابه
Knowledge-Powered Deep Learning for Word Embedding
The basis of applying deep learning to solve natural language processing tasks is to obtain high-quality distributed representations of words, i.e., word embeddings, from large amounts of text data. However, text itself usually contains incomplete and ambiguous information, which makes necessity to leverage extra knowledge to understand it. Fortunately, text itself already contains welldefined ...
متن کاملLearning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
We present a method that learns word embedding for Twitter sentiment classification in this paper. Most existing algorithms for learning continuous word representations typically only model the syntactic context of words but ignore the sentiment of text. This is problematic for sentiment analysis as they usually map words with similar syntactic context but opposite sentiment polarity, such as g...
متن کاملCluster-Driven Model for Improved Word and Text Embedding
Most of the existing word embedding models only consider the relationships between words and their local contexts (e.g. ten words around the target word). However, information beyond local contexts (global contexts), which reflect the rich semantic meanings of words, are usually ignored. In this paper, we present a general framework for utilizing global information to learn word and text repres...
متن کاملAn Exploration of Word Embedding Initialization in Deep-Learning Tasks
Word embeddings are the interface between the world of discrete units of text processing and the continuous, differentiable world of neural networks. In this work, we examine various random and pretrained initialization methods for embeddings used in deep networks and their effect on the performance on four NLP tasks with both recurrent and convolutional architectures. We confirm that pretraine...
متن کاملUnsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model
Deep neural network (DNN) based natural language processing models rely on a word embedding matrix to transform raw words into vectors. Recently, a deep structured semantic model (DSSM) has been proposed to project raw text to a continuously-valued vector for Web Search. In this technical report, we propose learning word embedding using DSSM. We show that the DSSM trained on large body of text ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer Science and Application
سال: 2016
ISSN: 2161-8801,2161-881X
DOI: 10.12677/csa.2016.611077