Select Subject

All (8831)

Agronomy (158)

Animal Husbandry,... (64)

Aquatic Science (16)

Astronomy (36)

Aviation (22)

Basic Medicine (184)

Biology (504)

Chemical Engineering (103)

Chemistry (635)

Civil and Archite... (105)

Clinical Medicine (236)

Computer Science ... (924)

Dynamic and Elect... (121)

Earth Science (119)

Economics (67)

Education (31)

Electrics, Commun... (927)

Energy Science an... (67)

Engineering and T... (15)

Environmental Sci... (140)

Food Science and ... (63)

Forestry (36)

Hydraulic Enginee... (40)

Mechanics (240)

Metallurgical Eng... (32)

Mine Engineering ... (38)

Nuclear Science a... (21)

Pharmacy (98)

Physics (920)

Preventive Medici... (37)

Psychology (20)

Security Science ... (17)

Sports Science (6)

Surveying and Map... (15)

Textile Science a... (16)

Traditional Chine... (56)

Transportation En... (57)

Select/Unselect all

For Selected Papers

Saved Papers

Please enter a name for this paper to be shown in your personalized Saved Papers list


	1. Improving Text Models with Latent Feature Vector Representations
	Peng Huaijin,Wang Jing,Shen Qiwei
	Computer Science and Technology 26 March 2019
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Probabilistic topic models are widely used to discover potential topics in a collection of documents, while latent feature vector representations have been used to achieve high performance in many NLP tasks. In this paper, we first make document topic vector representations by combining LDA and Topic2Vec, and then we perform document representations based on the topic vectors and the document vectors obtained through Doc2Vec training. Experimental results show that our new model has produced significant improvements in topic consistency and document classification tasks.
	TO cite this article:Peng Huaijin,Wang Jing,Shen Qiwei. Improving Text Models with Latent Feature Vector Representations[OL].[26 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4747891


	2. A BERT based Multi-task Learning Model for Judgment Prediction
	Yang Ze,Zhang Lei
	Computer Science and Technology 25 March 2019
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Judgment prediction is a difficult problem in judicial field. Given a fact description, judge need to conduct several documents to decide the related articles. This task is complex and requires a lot of energy. Previous works always treat this task as a multi-label learning paradigm for judgment prediction. These work usually neglect the external knowledge, thus the performance may be limited. In this paper, this topic use a multi-task learning framework with pretrained external knowledge to address this issues, and this topic propose a BERT based multi-task learning model(BMM for short). Specially, BMM use a pretrained BERT model to obtain external knowledge, then a multi-task learning framework is incorporated to learn multi-label classification and language model jointly. Experimental results on three real-world datasets demonstrate that the proposed model achieves significant improvements over state-of-the-art methods.
	TO cite this article:Yang Ze,Zhang Lei. A BERT based Multi-task Learning Model for Judgment Prediction[OL].[25 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4747962


	3. Chinese-Japanese News Comparable Corpus Construction Using Event Extraction
	Yang Jian,Xu Jinan
	Computer Science and Technology 12 May 2016
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Parallel bilingual corpus provides rich matching information of two corresponding languages. Usually, acquiring high-quality and large-scale parallel bilingual corpus keeps more difficulties. In this paper, we proposed a method to construct Chinese-Japanese news comparable corpus using event extraction technologies. Firstly, we extract Chinese and Japanese news using web crawler, then to extract news feature sets according to event extraction technology which combined with the Japanese-Chinese dictionary, named-entity dictionary, and Hanzi-Kanji mapping table of Japanese-Chinese characters, by calculating the similarity of the extracted news events, we realize a method of similarity detection using the feature of Japanese-Chinese news events and generate the extraction results of bilingual document alignment. Finally, we use the extraction results to train classifier model, which is used for identification of document alignment of Japanese-Chinese news. Experimental results show that our method is effective and it can overcome the shortcoming of traditional methods.
	TO cite this article:Yang Jian,Xu Jinan. Chinese-Japanese News Comparable Corpus Construction Using Event Extraction[OL].[12 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4686479


	4. PredicTV: A Behavior-Oriented Real Time Recommender for TV Programs
	Wenjing Fang, Zhiyuan Cai, Xiaodong Wang, Kenny Q. Zhu
	Computer Science and Technology 20 December 2015
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:With112channelsandabout30,000programstowatcheveryweek,televisionviewersin China are overwhelmed with choices. Finding out what to watch can be a time-consuming and frustrating process. In this paper, we present a system that leverages individuals’ viewing behaviors and the useful information about the TV programs to make real-time, dynamic program recommendations to the TV viewers. The system builds a vector-space based preference model for each user by combining the viewing patterns and the contents of the program that viewed by the users. Recommendation of future programs is done by selecting the best set of programs that matches the user’s viewing model.
	TO cite this article:Wenjing Fang, Zhiyuan Cai, Xiaodong Wang, et al. PredicTV: A Behavior-Oriented Real Time Recommender for TV Programs[OL].[20 December 2015] http://en.paper.edu.cn/en_releasepaper/content/4666173


	5. Article Errors Correction Based on Imbalanced Data Learning
	Chen Liangyu, Zhou Deyu
	Computer Science and Technology 24 December 2013
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:To correct the article usage error in English texts,this paper proposes a novel approach based on classification for article error correction.However, there is only small quantity of labeled data available,while large quantity of data are available withoutannotations. To fully employ both types of data, we usethe balance-cascade algorithm to overcome the imbalanced data problem.Experiments were conducted on the NUS Corpus of Learner English and experimental results showed that the proposed method can achieve high precision rate.
	TO cite this article:Chen Liangyu, Zhou Deyu. Article Errors Correction Based on Imbalanced Data Learning[OL].[24 December 2013] http://en.paper.edu.cn/en_releasepaper/content/4577940


	6. A Bayesian Network for Automatic Term Recognition
	GUI Yaocheng,GAO Zhiqiang
	Computer Science and Technology 30 November 2011
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Terms with explicit meanings are used in the academic semantic search system to represent specific research domains.The major works of Automatic Term Recognition (ATR) focus on measuring the relationship between term and paper as the feature of term.The academic semantic search system does not provide full papers, and the short-text-corpus constructed by titles and abstracts of papers reduces the influence of the feature.This paper proposes a novel ATR approach.Firstly, new types of features are provided by measuring the relationships between term and other entities.Secondly, based on the relations between the features of term, the TRBN (term recognition bayesian network) model which is represented by Bayesian Network is proposed to integrate the features.The results of experiments, which are implemented on the corpus containing 7,750,000 titles and 4,500,000 abstracts from the domain of telecommunication and computer science, illustrate the good performance of this new approach that is 10 percent of precision outperforms the baseline method.
	TO cite this article:GUI Yaocheng,GAO Zhiqiang. A Bayesian Network for Automatic Term Recognition[OL].[30 November 2011] http://en.paper.edu.cn/en_releasepaper/content/4452995


	7. Sentence-level Sentiment Analysis Based on Sub-view Framework
	Xing Xinyan,Zhang Xianchao,Liu Xiaohua
	Computer Science and Technology 13 October 2010
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:This paper presents a method for improving the performance of sentence-level sentiment analysis. Sentiment analysis is thought to require a deep understanding of the sentence structure (e.g., word order and non-local dependency). To attack this problem without the sentence parsing, we propose an approach whereby a given sentence is decomposed into a series of sub-sequences or sub-view representations. Sentence-level polarity is then determined by classifying within sub-views and fusing the obtained sub-view polarities. Two specific methods are instantiated: stacking-based maximum entropy model and hidden conditional random fields (HCRFs) based on contextual features. Extensive evaluations were carried out on two benchmark dataset, one is for sentence subjectivity classification and the other is for sentence polarity detection. Experimental results show that the performance of our proposed method is comparable to the state-of-the-art approaches.
	TO cite this article:Xing Xinyan,Zhang Xianchao,Liu Xiaohua. Sentence-level Sentiment Analysis Based on Sub-view Framework[OL].[13 October 2010] http://en.paper.edu.cn/en_releasepaper/content/4388143

Select/Unselect all

For Selected Papers

Saved Papers

Please enter a name for this paper to be shown in your personalized Saved Papers list

Rank

Alert Name:
Alerting to:
Authentication email will be sent to your email address in 24 hours
Frequency:
Email Message Format:	Plain text Graphical(HTML)

Complete the form below and we will recommend the selected titles to your friends on your behalf. * Indicates a required field.
Your name*:
Your email address*:
Recipient's name*:
Recipient's email address*:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find the page(s) useful.

Your name:
Your email address:
Recipient's name:
Recipient's email address:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find this page useful.

Disclaimer: This message was sent to your friend using the "Send it to a friend" facility on the Sciencepaper Online’ WWW site, http://www.paper.edu.cn/en. The Sciencepaper Online is not responsible for the content of this email, and anything said in this email does not necessarily reflect the Sciencepaper Online's views.

	Check out RSS, or use RSS reader to subscribe this item