Titlebook: Artificial Intelligence and Natural Language; 7th International Co Dmitry Ustalov,Andrey Filchenkov,Jan ?i?ka Conference proceedings 2018 S

易于出錯(cuò)

瑣事

撫育

Encyclopedia of Heroism Studiesth, heart failure and chronic liver diseases (cirrhosis and fibrosis) prediction tasks. We propose ontology-based regularization method that can be used to pre-train MCV embeddings. The approach we use to predict these diseases and conditions can be applied to solve other prediction tasks.

葡萄糖

謙卑

A Multi-feature Classifier for Verbal Metaphor Identification in Russian TextsRussian text. We introduce the custom-created training dataset, describe the feature engineering techniques, and discuss the results. The following set of features is applied: distributional semantic features, lexical and morphosyntactic co-occurrence frequencies, flag words, quotation marks, and se

擦試不掉

condone

Named Entity Recognition in Russian with Word Representation Learned by a Bidirectional Language Modhave become a standard component of neural network architectures for natural language processing tasks. However, in most cases, a recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively few labeled data. Also, there are many

有害

Supervised Mover’s Distance: A Simple Model for Sentence Comparisonory (LSTM) through a Relation Network. The Relation Network module tries to extract similarity between multiple contextual representations obtained from LSTM. The aim is to build a model which is simple to implement, light in terms of parameters and works across multiple supervised sentence comparis

Cloudburst

voluble