Titlebook: Chinese Computational Linguistics; 19th China National Maosong Sun,Sujian Li,Gaoqi Rao Conference proceedings 2020 Springer Nature Switzer

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Sara Nicholas,Patricia M. DeMarcos, while not all data records are important for text generation and inappropriate input may bring noise into the final output. To solve this problem, we propose a two-step approach which first selects and orders the important data records and then generates text from the noise-reduced data. Here we

手段

Growing Cleaner More Efficient Manufacturingth not only a consistent topic but also novel wordings. Although many approaches have been proposed and obvious progress has been made on this task, there is still a large room for improvement, especially for improving thematic consistency and wording diversity. To mitigate the gap between generated

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Garretson Oester,Amanda Woodrum coherence of messages. In this paper, we focus on ., an important subtask of causal explanation analysis, which determines whether a causal explanation exists in one message. We design a .yramid .alient-.ware .etwork (PSAN) to detect causal explanations on messages. PSAN can assist in causal explan

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https://doi.org/10.1007/978-3-658-13981-0del training, and their performance on rare entities is usually unsatisfactory. Entity dictionaries can cover many entities including both popular ones and rare ones, and are useful for NER. However, many entity names are context-dependent and it is not optimal to directly apply dictionaries without

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https://doi.org/10.1007/978-3-658-13981-0s overlap which is a common phenomenon in practice. To solve this problem, this paper defines event relation triple to explicitly represent relations among triggers, arguments and roles which are incorporated into the model to learn their inter-dependencies. A novel joint framework for multiple Chin

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https://doi.org/10.1007/978-3-540-33273-2s. Among existing studies, the Multi-Head Selection (MHS) framework is efficient in extracting entities and relations simultaneously. However, the method is weak for its limited performance. In this paper, we propose several effective insights to address this problem. First, we propose an entity-spe

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A Joint Model for Graph-Based Chinese Dependency Parsing can eliminate error propagation and share knowledge, where the transition-based model with feature templates maintains the best performance. Recently, the graph-based joint model[.] on word segmentation and dependency parsing has achieved better performance, demonstrating the advantages of the grap

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