標題: Titlebook: Natural Language Processing and Chinese Computing; 6th CCF Internationa Xuanjing Huang,Jing Jiang,Yu Hong Conference proceedings 2018 Sprin [打印本頁] 作者: 宗派 時間: 2025-3-21 17:58
書目名稱Natural Language Processing and Chinese Computing影響因子(影響力)
書目名稱Natural Language Processing and Chinese Computing影響因子(影響力)學科排名
書目名稱Natural Language Processing and Chinese Computing網(wǎng)絡公開度
書目名稱Natural Language Processing and Chinese Computing網(wǎng)絡公開度學科排名
書目名稱Natural Language Processing and Chinese Computing被引頻次
書目名稱Natural Language Processing and Chinese Computing被引頻次學科排名
書目名稱Natural Language Processing and Chinese Computing年度引用
書目名稱Natural Language Processing and Chinese Computing年度引用學科排名
書目名稱Natural Language Processing and Chinese Computing讀者反饋
書目名稱Natural Language Processing and Chinese Computing讀者反饋學科排名
作者: 下船 時間: 2025-3-21 22:28 作者: HACK 時間: 2025-3-22 03:50 作者: labyrinth 時間: 2025-3-22 05:40
Natural Language Processing and Chinese Computing978-3-319-73618-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Soliloquy 時間: 2025-3-22 11:32 作者: 過剩 時間: 2025-3-22 15:38
Conference proceedings 2018ina, in November 2017.. The 47 full papers and 39 short papers presented were carefully reviewed and selected from 252 submissions.? The papers are organized around the following topics: IR/search/bot; knowledge graph/IE/QA; machine learning; machine translation; NLP applications; NLP fundamentals; social networks; and text mining..作者: rheumatism 時間: 2025-3-22 18:01
Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Informationsults on different datasets show that the proposed models outperform joint models without either hierarchical or contextual information. Besides, finding the balance between two loss functions of two subtasks is important to achieve best overall performances.作者: 雜役 時間: 2025-3-22 22:28 作者: persistence 時間: 2025-3-23 04:17
First Place Solution for NLPCC 2017 Shared Task Social Media User Modelingproach results, including user-based collaborative filtering (CF) and social-based CF to predict the locations. Subtask two is to predict the users’ gender. We divided the users into two groups, depending on whether the user has posted or not. We treat this task subtask as a classification task. Our results achieved first place in both subtasks.作者: MEET 時間: 2025-3-23 06:22
A Chinese Question Answering System for Single-Relation Factoid Questions are used to choose the final predicted answers. Our approach achieved the F1-score of 47.23% on test data which obtained the first place in the contest of NLPCC 2017 Shared Task 5 (KBQA sub-task). Furthermore, there are also a series of experiments which can help other developers understand the contribution of every part of our system.作者: 彩色 時間: 2025-3-23 09:57
Enhancing Document-Based Question Answering via Interaction Between Question Words and POS Tagstion. Experimental results on DBQA Task have shown that our model has achieved better results, compared with several state-of-the-art systems. In addition, it also achieves the best result on NLPCC 2017 Shared Task on DBQA.作者: Ballerina 時間: 2025-3-23 16:42 作者: 出汗 時間: 2025-3-23 19:43
Random Projections with Bayesian Priorsto a smaller subspace via random projections, and their relative similarity computed via distance metrics. We propose using marginal information and Bayesian probability to improve the estimates of the inner product between pairs of vectors, and demonstrate our results on actual datasets.作者: bronchodilator 時間: 2025-3-24 01:24
Augmenting Neural Sentence Summarization Through Extractive Summarization to achieve the fusion of the contents in different views, which can be easily adapted to other domains. Experimental results on CNN/Daily Mail dataset demonstrate both our proposed strategies can significantly improve the performance of neural sentence summarization.作者: 表被動 時間: 2025-3-24 05:26 作者: Blasphemy 時間: 2025-3-24 08:49
Geography Gaokao-Oriented Knowledge Acquisition for Comparative Sentences Based on Logic Programming programming is employed to filter out non-comparative sentences, and for the latter task, the information of dependency grammar and heuristic position is adopted to represent the relations among comparative elements. The experimental results show that our system achieves outstanding performance for practical use.作者: 網(wǎng)絡添麻煩 時間: 2025-3-24 12:00 作者: 暴露他抗議 時間: 2025-3-24 16:17
Cascaded LSTMs Based Deep Reinforcement Learning for Goal-Driven Dialoguee is no explicit NLU and dialogue states in the network. Experimental results show that our model outperforms both traditional Markov Decision Process (MDP) model and single LSTM with Deep Q-Network on meeting room booking tasks. Visualization of dialogue embeddings illustrates that the model can learn the representation of dialogue states.作者: CAPE 時間: 2025-3-24 20:21
0302-9743 Processing, NLPCC 2017, held in Dalian, China, in November 2017.. The 47 full papers and 39 short papers presented were carefully reviewed and selected from 252 submissions.? The papers are organized around the following topics: IR/search/bot; knowledge graph/IE/QA; machine learning; machine transl作者: 聰明 時間: 2025-3-24 23:21
Conference proceedings 2018ina, in November 2017.. The 47 full papers and 39 short papers presented were carefully reviewed and selected from 252 submissions.? The papers are organized around the following topics: IR/search/bot; knowledge graph/IE/QA; machine learning; machine translation; NLP applications; NLP fundamentals; 作者: Axillary 時間: 2025-3-25 06:03 作者: alleviate 時間: 2025-3-25 11:08 作者: aristocracy 時間: 2025-3-25 13:19
Yimeng Zhuang,Xianliang Wang,Han Zhang,Jinghui Xie,Xuan Zhuieurwesen der Universit?t Hannover. T?tigkeit von 1989 bis 1991 in Australien bei der Firma Thiess Contractors Pty. Ltd., Sydney, und von 1991 bis 1994 im Ingenieurbüro Prof. Dr. Lackner & Partner, Bremen. Bis 978-3-540-26976-2作者: SLAG 時間: 2025-3-25 17:45
Lingfei Qian,Anran Wang,Yan Wang,Yuhang Huang,Jian Wang,Hongfei Linieurwesen der Universit?t Hannover. T?tigkeit von 1989 bis 1991 in Australien bei der Firma Thiess Contractors Pty. Ltd., Sydney, und von 1991 bis 1994 im Ingenieurbüro Prof. Dr. Lackner & Partner, Bremen. Bis 978-3-540-26976-2作者: 大包裹 時間: 2025-3-25 23:49 作者: 預防注射 時間: 2025-3-26 00:20 作者: Commonwealth 時間: 2025-3-26 07:02 作者: helper-T-cells 時間: 2025-3-26 10:52 作者: 不可思議 時間: 2025-3-26 16:21 作者: Fecundity 時間: 2025-3-26 20:14
ieurwesen der Universit?t Hannover. T?tigkeit von 1989 bis 1991 in Australien bei der Firma Thiess Contractors Pty. Ltd., Sydney, und von 1991 bis 1994 im Ingenieurbüro Prof. Dr. Lackner & Partner, Bremen. Bis 978-3-540-26976-2作者: 延期 時間: 2025-3-26 21:25
Junnan Zhu,Long Zhou,Haoran Li,Jiajun Zhang,Yu Zhou,Chengqing Zongtro and in vivo studies have presented that the lignans are capable of inhibiting the growth of cancer cells by down-regulating protein expressions, suppressing the production of gene products, and through cell cycle arrest. Sesame lignans have been proven to manifest these anticancer effects agains作者: 發(fā)微光 時間: 2025-3-27 04:23
is 1989 wissenschaftliche Mitarbeiterin am Franzius-Institut für Wasserbau und Küsteningenieurwesen der Universit?t Hannover. T?tigkeit von 1989 bis 1991 in Australien bei der Firma Thiess Contractors Pty. Ltd., Sydney, und von 1991 bis 1994 im Ingenieurbüro Prof. Dr. Lackner & Partner, Bremen. Bis 作者: 600 時間: 2025-3-27 05:49
Xuelian Li,Qian Liu,Man Zhu,Feifei Xu,Yunxiu Yu,Shang Zhang,Zhaoxi Ni,Zhiqiang Gaois 1989 wissenschaftliche Mitarbeiterin am Franzius-Institut für Wasserbau und Küsteningenieurwesen der Universit?t Hannover. T?tigkeit von 1989 bis 1991 in Australien bei der Firma Thiess Contractors Pty. Ltd., Sydney, und von 1991 bis 1994 im Ingenieurbüro Prof. Dr. Lackner & Partner, Bremen. Bis 作者: 王得到 時間: 2025-3-27 11:14 作者: PRISE 時間: 2025-3-27 17:04 作者: Ibd810 時間: 2025-3-27 19:21 作者: extinguish 時間: 2025-3-27 23:31 作者: 娘娘腔 時間: 2025-3-28 04:16 作者: ornithology 時間: 2025-3-28 06:57
Chinese Question Classification Based on Semantic Joint Featuresw that our semantic joint feature extraction method outperforms classical syntactic based or content vector based method and superior to convolutional neural network based sentence classification method.作者: Resign 時間: 2025-3-28 12:04 作者: 用手捏 時間: 2025-3-28 15:57
Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Informationork has made use of either hierarchical or contextual information when jointly modeling intent classification and slot filling, proving that either of them is helpful for joint models. This paper proposes a cluster of joint models to encode both types of information at the same time. Experimental re作者: 壓迫 時間: 2025-3-28 19:17
Augmenting Neural Sentence Summarization Through Extractive Summarizationvious works can only utilize lead sentences as the input to generate the abstractive summarization, which ignores crucial information of the document. To alleviate this problem, we propose a novel approach to improve neural sentence summarization by using extractive summarization, which aims at taki作者: meditation 時間: 2025-3-28 23:40
Cascaded LSTMs Based Deep Reinforcement Learning for Goal-Driven Dialoguestems. There are three parts in this model. A Long Short-Term Memory (LSTM) at the bottom of the network encodes utterances in each dialogue turn into a turn embedding. Dialogue embeddings are learned by a LSTM at the middle of the network, and updated by the feeding of all turn embeddings. The top 作者: 陶器 時間: 2025-3-29 05:48 作者: Tonometry 時間: 2025-3-29 08:34
An Ensemble Approach to Conversation Generation paper gives a detailed description about an ensemble system for short text conversation generation. The proposed system consists of four subsystems, a quick response candidates selecting module, an information retrieval system, a generation-based system and an ensemble module. An advantage of this 作者: FORGO 時間: 2025-3-29 12:00 作者: 表狀態(tài) 時間: 2025-3-29 17:35
Large-Scale Simple Question Generation by Template-Based Seq2seq Learninge’s no large-scale question-answer corpora available for Chinese question answering over knowledge bases. In this paper, we present a 28M Chinese Q&A corpora based on the Chinese knowledge base provided by NLPCC2017 KBQA challenge. We propose a novel neural network architecture which combines templa作者: PAC 時間: 2025-3-29 22:45 作者: enumaerate 時間: 2025-3-30 00:34
Geography Gaokao-Oriented Knowledge Acquisition for Comparative Sentences Based on Logic Programming high knowledge skill. As a preliminary attempt to address this problem, we build a geography Gaokao-oriented knowledge acquisition system for comparative sentences based on logic programming to help solve real geography examinations. Our work consists of two consecutive tasks: identify comparative 作者: Conscientious 時間: 2025-3-30 08:07
Chinese Question Classification Based on Semantic Joint Featuresxts and those short texts like comments on product. They generally contain interrogative words such as who, which, where or how to specify the information required, and include complete grammatical components in the sentence. Based on these characteristics, we propose a more effective feature extrac作者: 娘娘腔 時間: 2025-3-30 08:44 作者: conflate 時間: 2025-3-30 13:09
Enhancing Document-Based Question Answering via Interaction Between Question Words and POS Tagssentence-pair modeling, but ignore the peculiars of question-answer pairs. This paper proposes to model the interaction between question words and POS tags, as a special kind of information that is peculiar to question-answer pairs. Such information is integrated into a neural model for answer selec作者: larder 時間: 2025-3-30 20:10
A Deep Learning Way for Disease Name Representation and Normalizationthat dictionary lookup method couldn’t get a high accuracy on this task. Dnorm is the first machine learning approach for this task. It is not robust enough due to strong dependence on training dataset. In this article, we propose a deep learning way for disease name representation and normalization作者: Amendment 時間: 2025-3-30 22:47 作者: 英寸 時間: 2025-3-31 01:35
Random Projections with Bayesian Priorsertain forms of distances or distance kernels such as Euclidean distances, inner products [.], and . distances [.] between high dimensional vectors are approximately preserved in this smaller dimensional subspace. Word vectors which are represented in a bag of words model can thus be projected down
