With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently...With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently a lot of approaches from natural language processing＇s perspec- tive have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analy- sis methods. In this research, we further investigate the struc- tural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is play- ing important role in identifying the overall statement＇s po- larity. As a result a novel sentiment analysis model is pro- posed based on recurrent neural network, which takes the par- tial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.展开更多
When people want to move to a new job, it is often difficult since there is too much job information available. To select an appropriate job and then submit a resume is tedious. It is particularly difficult for univer...When people want to move to a new job, it is often difficult since there is too much job information available. To select an appropriate job and then submit a resume is tedious. It is particularly difficult for university students since they normally do not have any work experience and also are unfamiliar with the job market. To deal with the informa- tion overload for students during their transition into work, a job recommendation system can be very valuable. In this research, after fully investigating the pros and cons of current job recommendation systems for university students, we propose a student profiling based re-ranking framework. In this system, the students are recommended a list of potential jobs based on those who have graduated and obtained job offers over the past few years. Furthermore, recommended employers are also used as input for job recommendation result re-ranking. Our experimental study on real recruitment data over the past four years has shown this method＇s potential.展开更多
A precise method for accurately tracking dim- small targets, based on spectral fingerprint is proposed where traditional full color tracking seems impossible. A fingerprint model is presented to adequately extract spe...A precise method for accurately tracking dim- small targets, based on spectral fingerprint is proposed where traditional full color tracking seems impossible. A fingerprint model is presented to adequately extract spectral features. By creating a multidimensional feature space and extending the limited RGB information to the hyperspectral information, the improved precise tracking model based on a nonparamet- ric kernel density estimator is built using the probability his- togram of spectral features. A layered particle filter algorithm for spectral tracking is presented to avoid the object jumping abruptly. Finally, experiments are conducted that show that the tracking algorithm with spectral fingerprint features is ac- curate, fast, and robust. It meets the needs of dim-small target tracking adequately.展开更多
基金This work was partially supported by the Na- tional High Technology Research and Development Program of China （2011AA010502）, the National Natural Science Foundation of China （Grant No. 61103095）, and the Fundamental Research Funds for the Central Uni- versifies. We are grateful to Shenzhen Key Laboratory of Data Vitalization （Smart City） for supporting this research.
文摘With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to under- stand the polarity beneath these comments. Currently a lot of approaches from natural language processing＇s perspec- tive have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analy- sis methods. In this research, we further investigate the struc- tural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is play- ing important role in identifying the overall statement＇s po- larity. As a result a novel sentiment analysis model is pro- posed based on recurrent neural network, which takes the par- tial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.
基金Acknowledgements This work was partially supported by the State Key Laboratory of Software Development Environment of China （SKLSDE- 2015ZX-17）, the National Natural Science Foundation of China （Grant No. 61472021）, and the Fundamental Research Funds for the Central Universities.
文摘When people want to move to a new job, it is often difficult since there is too much job information available. To select an appropriate job and then submit a resume is tedious. It is particularly difficult for university students since they normally do not have any work experience and also are unfamiliar with the job market. To deal with the informa- tion overload for students during their transition into work, a job recommendation system can be very valuable. In this research, after fully investigating the pros and cons of current job recommendation systems for university students, we propose a student profiling based re-ranking framework. In this system, the students are recommended a list of potential jobs based on those who have graduated and obtained job offers over the past few years. Furthermore, recommended employers are also used as input for job recommendation result re-ranking. Our experimental study on real recruitment data over the past four years has shown this method＇s potential.
文摘A precise method for accurately tracking dim- small targets, based on spectral fingerprint is proposed where traditional full color tracking seems impossible. A fingerprint model is presented to adequately extract spectral features. By creating a multidimensional feature space and extending the limited RGB information to the hyperspectral information, the improved precise tracking model based on a nonparamet- ric kernel density estimator is built using the probability his- togram of spectral features. A layered particle filter algorithm for spectral tracking is presented to avoid the object jumping abruptly. Finally, experiments are conducted that show that the tracking algorithm with spectral fingerprint features is ac- curate, fast, and robust. It meets the needs of dim-small target tracking adequately.
