Titlebook: Chinese Computational Linguistics; 18th China National Maosong Sun,Xuanjing Huang,Yang Liu Conference proceedings 2019 Springer Nature Swi

一大塊

SKIFF

癡呆

perjury

Comparative Investigation of Deep Learning Components for End-to-end Implicit Discourse Relationshiptic work to investigate the influence of neural components on the performance of implicit discourse relation recognition. To address it, in this work we compare many different components and build two implicit discourse parsers base on the sequence and structure of sentence respectively. Experimenta

暴露他抗議

開頭

Sharing Pre-trained BERT Decoder for a Hybrid Summarizations two separated subtasks. In this paper, we propose a novel extractive-and-abstractive hybrid framework for single document summarization task by jointly learning to select sentence and rewrite summary. It first selects sentences by an extractive decoder and then generate summary according to each s

relieve

Title-Aware Neural News Topic Predictionet user interests and make personalized recommendations. However, massive news articles are generated everyday, and it too expensive and time-consuming to manually categorize all news. The news bodies usually convey the detailed information of news, and the news titles usually contain summarized and

Mutter

Colligational Patterns in China English: The Case of the Verbs of Communicationt in BNC. They are . and . for the verb ., . for the verb ., and . for the verb .. (3) Some colligational patterns occur less frequently in CCE than those in BNC, such as the patterns . and . for the verb . and . for the verb ., and . for the verb .. (4) No new colligational patterns have been found

Interdict

Enhancing Chinese Word Embeddings from Relevant Derivative Meanings of Main-Components in Charactershe attention mechanism. Our models can fine-grained enhance the precision of word embeddings without generating additional vectors. Experiments on word similarity and syntactic analogy tasks are conducted to validate the feasibility of our models. Furthermore, the results show that our models have a

抒情短詩

Association Relationship Analyses of Stylistic Syntactic Structuresied before. Combined with the linguistic theory, detailed analyses show that the association between parts of speech and syntactic structures mined by machine learning method has an excellent stylistic explanatory effect.