7,061 research outputs found
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Court Judgment Decision Support System Based on Medical Text Mining
Medical damage is a common problem faced by hospitals around the world and is widely watched by countries and the World Health Organization. As the number of medical damage dispute lawsuit cases rapidly grows, many countries in the world face the problem how to improve the efficiency of the judicial system under the premise of guaranteeing the quality of the trial. Therefore, in addition to reforming the system, the decision support system will effectively improve judicial decisions. This paper takes medical damage judgment documents in China as example, and proposes a court judgment decision support system (CJ-DSS) based on medical text mining and the automatic classification technology. The system can predict the trail results of the new lawsuit documents according to the previous cases verdict - rejected and non-rejected. Combined with the cases, the study in this paper found that combined feature extraction method does improve the performance of three kinds of classifiers - Support Value Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN), the degree of improved performance is different from using DF-CHI combined feature extraction method. In addition, integrated learning algorithm also improves the classification performance of the overall system
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Augmenting Naive Bayes Classifiers with Statistical Language Models
We augment naive Bayes models with statistical n-gram language models to address short- comings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier which allows for a local Markov dependence among observations; a model we re- fer to as the Chain Augmented Naive Bayes (CAN) Bayes classifier. CAN models have two advantages over standard naive Bayes classifiers. First, they relax some of the indepen- dence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed variables—while still permitting efficient inference and learning. Second, they permit straight- forward application of sophisticated smoothing techniques from statistical language modeling, which allows one to obtain better parameter estimates than the standard Laplace smoothing used in naive Bayes classification. In this paper, we introduce CAN models and apply them to various text classification problems. To demonstrate the language independent and task independent nature of these classifiers, we present experimental results on several text clas- sification problems—authorship attribution, text genre classification, and topic detection—in several languages—Greek, English, Japanese and Chinese. We then systematically study the key factors in the CAN model that can influence the classification performance, and analyze the strengths and weaknesses of the model
Sentiment analysis on Chinese web forums using elastic nets: Features, classification and interpretation: Working paper series--11-11
Consumer opinion has always been of great concern for businesses and others in the commercial sector. Among all social media which contain opinion-rich content, Web forums have become influential due to the large volume of discussions and high levels of interactivity. The Chinese market has now emerged as one of the largest ones over the world, therefore understanding the opinions and sentiments expressed by Chinese consumers has become increasingly important. In this study, we proposed a generic framework to analyze sentiment in Chinese Web forums. To detect online sentiment, we developed a classification method using Elastic Nets with rich feature representation. The proposed sentiment analysis framework was evaluated on two of the most famous Chinese forums with topics on Chinese stock market and laptop. Findings about interesting features were discussed
Character-level Convolutional Networks for Text Classification
This article offers an empirical exploration on the use of character-level
convolutional networks (ConvNets) for text classification. We constructed
several large-scale datasets to show that character-level convolutional
networks could achieve state-of-the-art or competitive results. Comparisons are
offered against traditional models such as bag of words, n-grams and their
TFIDF variants, and deep learning models such as word-based ConvNets and
recurrent neural networks.Comment: An early version of this work entitled "Text Understanding from
Scratch" was posted in Feb 2015 as arXiv:1502.01710. The present paper has
considerably more experimental results and a rewritten introduction, Advances
in Neural Information Processing Systems 28 (NIPS 2015
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
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