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Titlebook: Natural Language Processing and Chinese Computing; 7th CCF Internationa Min Zhang,Vincent Ng,Hongying Zan Conference proceedings 2018 Sprin

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發(fā)表于 2025-3-21 20:03:10 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing
副標(biāo)題7th CCF Internationa
編輯Min Zhang,Vincent Ng,Hongying Zan
視頻videohttp://file.papertrans.cn/662/661809/661809.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Natural Language Processing and Chinese Computing; 7th CCF Internationa Min Zhang,Vincent Ng,Hongying Zan Conference proceedings 2018 Sprin
描述.This two volume set of LNAI 11108 and LNAI 11109 constitutes the refereed proceedings of the 7th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2018, held in Hohhot, China, in August 2018.. ..The 55 full papers and 31 short papers presented were carefully reviewed and selected from 308 submissions. The papers of the first volume are organized in the following topics: conversational Bot/QA/IR; knowledge graph/IE; machine learning for NLP; machine translation; and NLP applications. The papers of the second volume are organized as follows: NLP for social network; NLP fundamentals; text mining; and short papers..
出版日期Conference proceedings 2018
關(guān)鍵詞artificial intelligence; classification; data mining; HCI; human-computer interaction; information retrie
版次1
doihttps://doi.org/10.1007/978-3-319-99501-4
isbn_softcover978-3-319-99500-7
isbn_ebook978-3-319-99501-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing影響因子(影響力)




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing被引頻次




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書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing年度引用




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing年度引用學(xué)科排名




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing讀者反饋




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing讀者反饋學(xué)科排名




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https://doi.org/10.1007/978-3-319-99501-4artificial intelligence; classification; data mining; HCI; human-computer interaction; information retrie
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Which Embedding Level is Better for Semantic Representation? An?Empirical Research on Chinese Phrased investigate the performance of the two basic units. Empirical results show that with all composing methods, word embedding out performs character embedding on both tasks, which indicates that word level is more suitable for composing semantic representation.
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Improving Word Embeddings for Antonym Detection Using Thesauri and SentiWordNetord in SentiWordNet. We conduct evaluations on three relevant tasks, namely GRE antonym detection, word similarity, and semantic textual similarity. The experiment results show that our antonym-sensitive embedding outperforms common word embeddings in these tasks, demonstrating the efficacy of our methods.
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