標(biāo)題: Titlebook: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D; 16th China National Maosong Sun,Xiao [打印本頁(yè)] 作者: supplementary 時(shí)間: 2025-3-21 18:33
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D影響因子(影響力)
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D影響因子(影響力)學(xué)科排名
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D網(wǎng)絡(luò)公開度
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D被引頻次
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D被引頻次學(xué)科排名
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D年度引用
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D年度引用學(xué)科排名
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D讀者反饋
書目名稱Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D讀者反饋學(xué)科排名
作者: Infant 時(shí)間: 2025-3-21 23:20 作者: NAG 時(shí)間: 2025-3-22 03:55
978-3-319-69004-9Springer International Publishing AG 2017作者: Hemiparesis 時(shí)間: 2025-3-22 04:56
Maosong Sun,Xiaojie Wang,Deyi XiongIncludes supplementary material: 作者: irradicable 時(shí)間: 2025-3-22 11:19
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/225770.jpg作者: Lumbar-Stenosis 時(shí)間: 2025-3-22 15:51 作者: Lumbar-Stenosis 時(shí)間: 2025-3-22 18:32
https://doi.org/10.1007/978-3-540-28396-6 In this paper, we focus on person name recognition in judgment documents. Owing to the lack of human-annotated data, we propose a joint learning approach, namely Aux-LSTM, to use a large scale of auto-annotated data to help human-annotated data (in a small size) for person name recognition. Specifi作者: 空氣傳播 時(shí)間: 2025-3-23 00:41 作者: VERT 時(shí)間: 2025-3-23 02:13 作者: 浸軟 時(shí)間: 2025-3-23 06:18 作者: 贊成你 時(shí)間: 2025-3-23 13:47 作者: 閃光你我 時(shí)間: 2025-3-23 15:44
Reactive Halogen Compounds in the Atmosphere (SMT), and have a critical impact on the adequacy of the translation results generated by SMT systems. In this paper, first we classify the word deletion into two categories, wanted and unwanted word deletions. For these two kinds of word deletions, we propose a maximum entropy based word deletion 作者: 疲勞 時(shí)間: 2025-3-23 21:27
https://doi.org/10.1007/978-1-4613-3192-6algorithm for NMT sets a unified learning rate for each gold target word during training. However, words under different probability distributions should be handled differently. Thus, we propose a cost-aware learning rate method, which can produce different learning rates for words with different co作者: 彩色的蠟筆 時(shí)間: 2025-3-24 00:07
Chemistry of Selenium and Tellurium Atoms, a .-max pooling convolutional neural network (CNN) to exploit word sequences and dependency structures for CDR extraction. Furthermore, an effective weighted context method is proposed to capture semantic information of word sequences. Our system extracts both intra- and inter-sentence level chemic作者: Cardioplegia 時(shí)間: 2025-3-24 03:43
https://doi.org/10.1007/978-1-4613-3427-9 Those models learn local and global features automatically by RNNs so that hand-craft features can be discarded, totally or partly. Recently, convolutional neural networks (CNNs) have achieved great success on computer vision. However, for NER problems, they are not well studied. In this work, we p作者: labyrinth 時(shí)間: 2025-3-24 09:09 作者: 逃避責(zé)任 時(shí)間: 2025-3-24 12:49
https://doi.org/10.1007/978-1-4613-2973-2osed model uses a hybrid neural network to automatically learn sentence features and does not rely on any Natural Language Processing (NLP) tools, such as dependency parser. Our model is further capable of modeling multiple relations and their corresponding entity pairs simultaneously. Experiments o作者: AVOID 時(shí)間: 2025-3-24 18:04
https://doi.org/10.1007/978-1-4842-1428-2models. Moreover, some researchers propose lifelong topic models (LTM) to mine prior knowledge from topics generated from multi-domain corpus without human intervene. LTM incorporates the learned knowledge from multi-domain corpus into topic models by introducing the Generalized Polya Urn (GPU) mode作者: forebear 時(shí)間: 2025-3-24 21:40 作者: glowing 時(shí)間: 2025-3-25 03:09 作者: concise 時(shí)間: 2025-3-25 05:07 作者: calumniate 時(shí)間: 2025-3-25 10:43 作者: 展覽 時(shí)間: 2025-3-25 12:19
Employing Auto-annotated Data for Person Name Recognition in Judgment Documents In this paper, we focus on person name recognition in judgment documents. Owing to the lack of human-annotated data, we propose a joint learning approach, namely Aux-LSTM, to use a large scale of auto-annotated data to help human-annotated data (in a small size) for person name recognition. Specifi作者: Complement 時(shí)間: 2025-3-25 19:15
Closed-Set Chinese Word Segmentation Based on Convolutional Neural Network Modell to each character, indicating its relative position within the word it belongs to. To do so, it first constructs shallow representations of characters by fusing unigram and bigram information in limited context window via an element-wise maximum operator, and then build up deep representations fro作者: opprobrious 時(shí)間: 2025-3-25 21:35 作者: 泥土謙卑 時(shí)間: 2025-3-26 02:05 作者: pericardium 時(shí)間: 2025-3-26 07:30 作者: 極小量 時(shí)間: 2025-3-26 10:14 作者: 預(yù)測(cè) 時(shí)間: 2025-3-26 14:22
Cost-Aware Learning Rate for Neural Machine Translationalgorithm for NMT sets a unified learning rate for each gold target word during training. However, words under different probability distributions should be handled differently. Thus, we propose a cost-aware learning rate method, which can produce different learning rates for words with different co作者: tackle 時(shí)間: 2025-3-26 19:33
Integrating Word Sequences and Dependency Structures for Chemical-Disease Relation Extraction a .-max pooling convolutional neural network (CNN) to exploit word sequences and dependency structures for CDR extraction. Furthermore, an effective weighted context method is proposed to capture semantic information of word sequences. Our system extracts both intra- and inter-sentence level chemic作者: 優(yōu)雅 時(shí)間: 2025-3-26 23:30 作者: 無(wú)效 時(shí)間: 2025-3-27 04:59
Improving Event Detection via Information Sharing Among Related Event Typesoblem, we propose a novel approach that allows for information sharing among related event types. Specifically, we employ a fully connected three-layer artificial neural network as our basic model and propose a type-group regularization term to achieve the goal of information sharing. We conduct exp作者: 相反放置 時(shí)間: 2025-3-27 06:27
Joint Extraction of Multiple Relations and Entities by Using a Hybrid Neural Networkosed model uses a hybrid neural network to automatically learn sentence features and does not rely on any Natural Language Processing (NLP) tools, such as dependency parser. Our model is further capable of modeling multiple relations and their corresponding entity pairs simultaneously. Experiments o作者: facetious 時(shí)間: 2025-3-27 11:50
A Fast and Effective Framework for Lifelong Topic Model with Self-learning Knowledgemodels. Moreover, some researchers propose lifelong topic models (LTM) to mine prior knowledge from topics generated from multi-domain corpus without human intervene. LTM incorporates the learned knowledge from multi-domain corpus into topic models by introducing the Generalized Polya Urn (GPU) mode作者: 荒唐 時(shí)間: 2025-3-27 17:19 作者: 序曲 時(shí)間: 2025-3-27 18:38
XLink: An Unsupervised Bilingual Entity Linking Systemable attention and several online entity linking systems have been published. In this paper, we build an online bilingual entity linking system XLink, which is based on . and .. XLink conducts two steps to link the mentions in the input document to entities in knowledge base, namely mention parsing 作者: 使出神 時(shí)間: 2025-3-28 01:36 作者: 不在灌木叢中 時(shí)間: 2025-3-28 03:39
Willi J?ger,Rolf Rannacher,Jürgen Warnatzs can guarantee a higher precision rate, which heightens even more after dependency relations are added as linguistic rules for filtering, having achieved 85.11%. This method also achieved a higher precision rate rather than only resorting to syntactic dependency analysis as a collocation extraction method.作者: 蕁麻 時(shí)間: 2025-3-28 10:19 作者: 聯(lián)想記憶 時(shí)間: 2025-3-28 12:09
A. Hanf,H. -R. Volpp,J. Wolfrumasing on the finding, we propose a pseudo context skip-gram model, which makes use of context words of semantic nearest neighbors of target words. Experiment results show our model achieves significant performance improvements in both word similarity and analogy tasks.作者: 束縛 時(shí)間: 2025-3-28 15:06 作者: Triglyceride 時(shí)間: 2025-3-28 20:44
Reactive Halogen Compounds in the AtmosphereIn our experiments on Chinese-to-English news and web translation tasks, the results show that our approach is capable of generating more adequate translations compared with the baseline system, and our proposed word deletion model yields a +0.99 BLEU improvement and a . TER reduction on the NIST machine translation evaluation corpora.作者: Minuet 時(shí)間: 2025-3-29 01:21
https://doi.org/10.1007/978-1-4842-1428-2elational inference for semantic information extraction. Graph based linking algorithm is utilized to ensure per mention with only one candidate entity. Experiments on datasets show the proposed model significantly out-performs the state-of-the-art relatedness approaches in term of accuracy.作者: Congeal 時(shí)間: 2025-3-29 06:58
Reactive Intuitionistic Tableaux,orrectness of linking results, we propose an unsupervised generative probabilistic method and utilize text and knowledge joint representations to perform entity disambiguation. Experiments show that our system gets a state-of-the-art performance and a high time efficiency.作者: Insatiable 時(shí)間: 2025-3-29 10:13
Reactivity and Grammars: An Exploration,to embed the semantics of sentences. Then the features are fed into a classifier which takes into account both the ranking loss and cost-sensitive. Experiments show that our method is effective and performs better than state-of-the-art methods.作者: tenuous 時(shí)間: 2025-3-29 11:23
Arabic Collocation Extraction Based on Hybrid Methodss can guarantee a higher precision rate, which heightens even more after dependency relations are added as linguistic rules for filtering, having achieved 85.11%. This method also achieved a higher precision rate rather than only resorting to syntactic dependency analysis as a collocation extraction method.作者: Wordlist 時(shí)間: 2025-3-29 15:52
Employing Auto-annotated Data for Person Name Recognition in Judgment Documentse auxiliary LSTM representation to boost the performance of classifier trained on the human-annotated data. Empirical studies demonstrate the effectiveness of our proposed approach to person name recognition in judgment documents with both human-annotated and auto-annotated data.作者: medieval 時(shí)間: 2025-3-29 19:44 作者: NOT 時(shí)間: 2025-3-30 03:39
Enhancing LSTM-based Word Segmentation Using Unlabeled Datantwise mutual information, accessor variety and punctuation variety into our model and compare their performances on different datasets including three datasets from CoNLL-2017 shared task and three datasets of simplified Chinese. We achieve the state-of-the-art performance on two of them and get comparable results on the rest.作者: 狗窩 時(shí)間: 2025-3-30 07:26
Context Sensitive Word Deletion Model for Statistical Machine TranslationIn our experiments on Chinese-to-English news and web translation tasks, the results show that our approach is capable of generating more adequate translations compared with the baseline system, and our proposed word deletion model yields a +0.99 BLEU improvement and a . TER reduction on the NIST machine translation evaluation corpora.作者: 昏暗 時(shí)間: 2025-3-30 10:28
Collective Entity Linking on Relational Graph Model with Mentionselational inference for semantic information extraction. Graph based linking algorithm is utilized to ensure per mention with only one candidate entity. Experiments on datasets show the proposed model significantly out-performs the state-of-the-art relatedness approaches in term of accuracy.作者: 凹室 時(shí)間: 2025-3-30 12:56 作者: NUDGE 時(shí)間: 2025-3-30 18:45
Using Cost-Sensitive Ranking Loss to Improve Distant Supervised Relation Extractionto embed the semantics of sentences. Then the features are fed into a classifier which takes into account both the ranking loss and cost-sensitive. Experiments show that our method is effective and performs better than state-of-the-art methods.作者: 共同給與 時(shí)間: 2025-3-30 22:20
Conference proceedings 2017guage resource and evaluation; Information retrieval and question answering; Text classification and summarization; Social computing and sentiment analysis; NLP applications; Minority language information processing..作者: RUPT 時(shí)間: 2025-3-31 02:22
0302-9743 ledge graph and information extraction; Language resource and evaluation; Information retrieval and question answering; Text classification and summarization; Social computing and sentiment analysis; NLP applications; Minority language information processing..978-3-319-69004-9978-3-319-69005-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Banquet 時(shí)間: 2025-3-31 09:00 作者: countenance 時(shí)間: 2025-3-31 12:05 作者: Transfusion 時(shí)間: 2025-3-31 15:06
