標(biāo)題: Titlebook: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D; 15th China National Maosong Sun,Xuan [打印本頁(yè)] 作者: 大破壞 時(shí)間: 2025-3-21 16:17
書(shū)目名稱(chēng)Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D影響因子(影響力)
書(shū)目名稱(chēng)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(shū)目名稱(chēng)Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D被引頻次
書(shū)目名稱(chēng)Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D被引頻次學(xué)科排名
書(shū)目名稱(chēng)Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D年度引用
書(shū)目名稱(chēng)Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D年度引用學(xué)科排名
書(shū)目名稱(chēng)Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D讀者反饋
書(shū)目名稱(chēng)Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D讀者反饋學(xué)科排名
作者: 四海為家的人 時(shí)間: 2025-3-21 22:08 作者: micronized 時(shí)間: 2025-3-22 01:48 作者: CHIP 時(shí)間: 2025-3-22 07:41
János Tóth,Attila László Nagy,Dávid Pappage model by 41?% and 30?% respectively when comparing model trained on the corpus without processing. The proposed approach significantly improves the performance of Mongolian language model and greatly enhances the accuracy of Mongolian speech recognition.作者: 放肆的我 時(shí)間: 2025-3-22 12:45 作者: Synchronism 時(shí)間: 2025-3-22 14:33
Lisha Yang,Ji Su,Xiaokun Yang,Hongfei Lino used to filter the illogical label sequences. The experimental results conducted on the BioCreative II GM corpus show that our system can achieve an F-score of 88.61?%, which outperforms CRF models using the complex hand-designed features and is 6.74?% higher than RNNs.作者: Synchronism 時(shí)間: 2025-3-22 17:31
Sentence Alignment Method Based on Maximum Entropy Model Using Anchor Sentenceserent weights to characters in different position based on the contribution to align sentences. In the experiment performed on ., the precision and recall of the proposed method reaches 95.9?% and 95.6?% respectively, which outperforms other sentence alignment methods significantly.作者: obtuse 時(shí)間: 2025-3-22 21:12 作者: 侵略主義 時(shí)間: 2025-3-23 05:19 作者: 碎石 時(shí)間: 2025-3-23 09:20
Recognizing Biomedical Named Entities Based on the Sentence Vector/Twin Word Embeddings Conditioned o used to filter the illogical label sequences. The experimental results conducted on the BioCreative II GM corpus show that our system can achieve an F-score of 88.61?%, which outperforms CRF models using the complex hand-designed features and is 6.74?% higher than RNNs.作者: larder 時(shí)間: 2025-3-23 11:34
Critique of the “Theory of Rate Processes”ults. Various vector representations for sentence, TFIDF, latent semantic indexing, and neural network word embedding, are conducted and the experimental results show an alternative solution to enhance the current machine translation with a performance improvement about 0.5 BLEU in French-to-English task and 0.7 BLEU in English-to-Chinese task.作者: 變異 時(shí)間: 2025-3-23 14:51
Critique of the “Theory of Rate Processes”selves, 10 postgraduate students with good command of Chinese and English all agreed or strongly agreed that the general meaning of the translation by Yes Translate was correct and understandable. And 9 out of the 10 students agreed or strongly agreed that the general meaning of each sentence was correct.作者: 抗生素 時(shí)間: 2025-3-23 21:13 作者: Lipoprotein(A) 時(shí)間: 2025-3-24 01:09
János Tóth,Attila László Nagy,Dávid Pappral network model and utilizes this model to identification and classification for Tibetan person attributes, and achieved good results. This research has a very important role in the search engine, information security, machine translation and many other applications.作者: 木訥 時(shí)間: 2025-3-24 04:04
I Can Guess What You Mean: A Monolingual Query Enhancement for Machine Translationults. Various vector representations for sentence, TFIDF, latent semantic indexing, and neural network word embedding, are conducted and the experimental results show an alternative solution to enhance the current machine translation with a performance improvement about 0.5 BLEU in French-to-English task and 0.7 BLEU in English-to-Chinese task.作者: Lignans 時(shí)間: 2025-3-24 07:21
Keeping the Meanings of the Source Text: An Introduction to Yes Translateselves, 10 postgraduate students with good command of Chinese and English all agreed or strongly agreed that the general meaning of the translation by Yes Translate was correct and understandable. And 9 out of the 10 students agreed or strongly agreed that the general meaning of each sentence was correct.作者: ABOUT 時(shí)間: 2025-3-24 10:49
Using Collaborative Training Method to Build Vietnamese Dependency Treebankally to do tenfold cross-test and obtained the accuracy of 76.33?%. Experimental results showed that the proposed method in this paper could take full advantage of unmarked corpus to effectively improve the quality of dependency treebank.作者: 參考書(shū)目 時(shí)間: 2025-3-24 17:18
Tibetan Person Attributes Extraction Based on BP Neural Networkral network model and utilizes this model to identification and classification for Tibetan person attributes, and achieved good results. This research has a very important role in the search engine, information security, machine translation and many other applications.作者: Ascribe 時(shí)間: 2025-3-24 23:04
0302-9743 CL 2016, and the 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016, held in Yantai City, China, in October 2016.?.The 29 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 85 submissions. The作者: 徹底明白 時(shí)間: 2025-3-25 00:49
Characteristics of Our Reaction Kineticsinformation. Our model achieves state-of-the-art accuracy on Chinese Penn Treebank and competitive accuracy on English Penn Treebank with only first-order features. Moreover, our model shows effectiveness in recovering dependencies involving out-of-vocabulary words.作者: 承認(rèn) 時(shí)間: 2025-3-25 04:33
https://doi.org/10.1007/978-1-4939-8643-9d decoding phases of the model. Moreover we can statistical induction the information of chunk to disambiguation of multi-category words and experiment shows the precision is improved from 81.6?% to 87.7?% by information of chunk.作者: Trabeculoplasty 時(shí)間: 2025-3-25 08:11 作者: 明確 時(shí)間: 2025-3-25 14:10
,Insertion of CO2 into E–X Bonds,sentence which is aimed to translate; it is finished by correcting the structure or paraphrasing it with a relevant meaning. For that reason, the degree of similarity of two samples highly affects on the results of translation. Thus, there are dependence between quality of the outputs and the similarity degree.作者: 解決 時(shí)間: 2025-3-25 19:31
Improved Graph-Based Dependency Parsing via Hierarchical LSTM Networksinformation. Our model achieves state-of-the-art accuracy on Chinese Penn Treebank and competitive accuracy on English Penn Treebank with only first-order features. Moreover, our model shows effectiveness in recovering dependencies involving out-of-vocabulary words.作者: chandel 時(shí)間: 2025-3-25 21:47 作者: 設(shè)施 時(shí)間: 2025-3-26 03:23 作者: Hallmark 時(shí)間: 2025-3-26 05:52
Investigation and Use of Methods for Defining the Extends of Similarity of Kazakh Language Sentencessentence which is aimed to translate; it is finished by correcting the structure or paraphrasing it with a relevant meaning. For that reason, the degree of similarity of two samples highly affects on the results of translation. Thus, there are dependence between quality of the outputs and the similarity degree.作者: 貪婪性 時(shí)間: 2025-3-26 10:31 作者: Obsequious 時(shí)間: 2025-3-26 14:34 作者: 緯度 時(shí)間: 2025-3-26 20:26
Conference proceedings 2016m on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016, held in Yantai City, China, in October 2016.?.The 29 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 85 submissions. They were organized in topical sections named:作者: tangle 時(shí)間: 2025-3-26 23:41
Improving Chinese Semantic Role Labeling with English Proposition Bankass approach to do Chinese SRL with a Recurrent Neural Network (RNN) model. We use English Proposition Bank (EPB) to improve the performance of Chinese SRL. Experimental result shows a significant improvement over the state-of-the-art methods on Chinese Proposition Bank (CPB), which reaches 78.39?% 作者: BALE 時(shí)間: 2025-3-27 02:36 作者: 效果 時(shí)間: 2025-3-27 07:28
Improved Graph-Based Dependency Parsing via Hierarchical LSTM Networks learn word representations, allowing our model to avoid the problem of limited-vocabulary and capture both distributional and compositional semantic information. Our model achieves state-of-the-art accuracy on Chinese Penn Treebank and competitive accuracy on English Penn Treebank with only first-o作者: epicardium 時(shí)間: 2025-3-27 12:17 作者: meritorious 時(shí)間: 2025-3-27 16:05 作者: 拍下盜公款 時(shí)間: 2025-3-27 20:51
Keeping the Meanings of the Source Text: An Introduction to Yes Translate get lost or distorted in machine translation, including state-of-the-art Google Translate and Baidu Translate. Yes Translate is a Chinese-English translation tool to be maximally loyal to the source text while maintaining adequate fluency. This is implementable by avoiding risky actions of word del作者: saturated-fat 時(shí)間: 2025-3-28 00:20 作者: CRUE 時(shí)間: 2025-3-28 04:00 作者: deciduous 時(shí)間: 2025-3-28 06:39 作者: 生意行為 時(shí)間: 2025-3-28 11:14
Improved Joint Kazakh POS Tagging and Chunkingmodel. A improved beam-search algorithm use dynamic beam instead of unified beam to obtain search space of small-but-excellent during both training and decoding phases of the model. Moreover we can statistical induction the information of chunk to disambiguation of multi-category words and experimen作者: Genome 時(shí)間: 2025-3-28 14:49 作者: POLYP 時(shí)間: 2025-3-28 22:49
Tibetan Person Attributes Extraction Based on BP Neural Networkn on the network. In the face of the massive network information, extracting the information that people want is an urgent problem to be solved. Currently, Chinese person attributes extraction studies have some good results, but there is still much space to Tibetan person attributes extraction. The 作者: 個(gè)阿姨勾引你 時(shí)間: 2025-3-29 00:27
Semi-supervised Learning for Mongolian Morphological Segmentationore a novel semi-supervised method for a practical application, i.e., statistical machine translation (SMT), based on a low-resource learning setting, in which a small amount of labeled data and large amount of unlabeled data are available. First, a CRF-based supervised learning is exploited to pred作者: nocturnal 時(shí)間: 2025-3-29 04:38 作者: reperfusion 時(shí)間: 2025-3-29 09:24
Recognizing Biomedical Named Entities Based on the Sentence Vector/Twin Word Embeddings Conditioned network has been applied on the entity recognition to avoid the complex hand-designed features, which are derived from various linguistic analyses. However, performance of the conventional neural network systems is always limited to exploiting long range dependencies in sentences. In this paper, we作者: 會(huì)犯錯(cuò)誤 時(shí)間: 2025-3-29 15:22
https://doi.org/10.1007/978-981-19-9673-3tion errors decrease significantly after English sentences are parsed into NT clauses. This result reveals that non-SV clauses are the main source of MT errors, and suggests that English long sentences should be parsed into NT clauses before they are translated.作者: 損壞 時(shí)間: 2025-3-29 17:35
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D15th China National 作者: 哪有黃油 時(shí)間: 2025-3-29 19:52
Error Analysis of English-Chinese Machine Translationtion errors decrease significantly after English sentences are parsed into NT clauses. This result reveals that non-SV clauses are the main source of MT errors, and suggests that English long sentences should be parsed into NT clauses before they are translated.作者: 不確定 時(shí)間: 2025-3-30 02:54 作者: Triglyceride 時(shí)間: 2025-3-30 04:10
978-3-319-47673-5Springer International Publishing AG 2016
