Titlebook: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D; 17th China National Maosong Sun,Ting

新鲜

Measured

Collaborative Matching for Sentence Alignmentneral the length proportionality assumption that the lengths of sentences in one language tend to be proportional to that of their translations, and are known to bear poor adaptivity to new languages and corpora. In this paper, we attempt to interpret this assumption from a new perspective via the n

蹒跚

某人

Improving Low-Resource Neural Machine Translation with Weight Sharingfective for low-resource language. In order to alleviate the problem, we present two approaches which can improve the performance of low-resource NMT system. The first approach employs the weight sharing of decoder to enhance the target language model of low-resource NMT system. The second approach

食道

Identifying Word Translations in Scientific Literature Based on Labeled Bilingual Topic Model and Cond correlation in languages, this paper proposed the labeled bilingual topic model and co-occurrence feature based similarity metric which could be adopted to the word translation identifying task. First of all, it could assume that the keywords in the scientific literature are relevant to the abstr

复习

Term Translation Extraction from Historical Classics Using Modern Chinese Explanationhallenging part in the translation of historical classics. However, it is tough to recognize the terms directly from ancient Chinese due to the flexible syntactic of ancient Chinese and the word segmentation errors of ancient Chinese will lead to more errors in term translation extraction. Consideri

偏狂症

strdulate

Misgiving

Knowledge Graph Embedding with Logical Consistencygical background which is made up of a knowledge graph and a logical theory. Users must take great effort to filter consistent triples before adding new triples to the knowledge graph. To alleviate users’ burden, we propose an approach to enhancing existing embedding-based methods to encode logical

lavish

An End-to-End Entity and Relation Extraction Network with Multi-head Attentiondesigned features, which are usually time-consuming and may lead to poor generalization. Besides, most existing systems adopt pipeline methods, which treat the task as two separated tasks, i.e., named entity recognition and relation extraction. However, the pipeline methods suffer two problems: (1)