Titlebook: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big D; 16th China National Maosong Sun,Xiao

優(yōu)雅

無效

Improving Event Detection via Information Sharing Among Related Event Typesoblem, we propose a novel approach that allows for information sharing among related event types. Specifically, we employ a fully connected three-layer artificial neural network as our basic model and propose a type-group regularization term to achieve the goal of information sharing. We conduct exp

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Joint Extraction of Multiple Relations and Entities by Using a Hybrid Neural Networkosed model uses a hybrid neural network to automatically learn sentence features and does not rely on any Natural Language Processing (NLP) tools, such as dependency parser. Our model is further capable of modeling multiple relations and their corresponding entity pairs simultaneously. Experiments o

facetious

A Fast and Effective Framework for Lifelong Topic Model with Self-learning Knowledgemodels. Moreover, some researchers propose lifelong topic models (LTM) to mine prior knowledge from topics generated from multi-domain corpus without human intervene. LTM incorporates the learned knowledge from multi-domain corpus into topic models by introducing the Generalized Polya Urn (GPU) mode

荒唐

序曲

XLink: An Unsupervised Bilingual Entity Linking Systemable attention and several online entity linking systems have been published. In this paper, we build an online bilingual entity linking system XLink, which is based on . and .. XLink conducts two steps to link the mentions in the input document to entities in knowledge base, namely mention parsing

使出神

不在灌木叢中

Willi J?ger,Rolf Rannacher,Jürgen Warnatzs can guarantee a higher precision rate, which heightens even more after dependency relations are added as linguistic rules for filtering, having achieved 85.11%. This method also achieved a higher precision rate rather than only resorting to syntactic dependency analysis as a collocation extraction method.

蕁麻

聯(lián)想記憶

A. Hanf,H. -R. Volpp,J. Wolfrumasing on the finding, we propose a pseudo context skip-gram model, which makes use of context words of semantic nearest neighbors of target words. Experiment results show our model achieves significant performance improvements in both word similarity and analogy tasks.