Titlebook: Artificial Intelligence and Natural Language; 6th Conference, AINL Andrey Filchenkov,Lidia Pivovarova,Jan ?i?ka Conference proceedings 2018

聽(tīng)覺(jué)

Combined Feature Representation for Emotion Classification from Russian Speechtics that preserve temporal structure of the utterance. On the other hand, utterance-level features represent functionals applied to the low-level descriptors and contain important information about speaker emotional state. Utterance-level features are particularly useful for determining emotion int

逢迎白雪

Active Learning with Adaptive Density Weighted Sampling for Information Extraction from Scientific Pn and result extraction from scientific publications in Russian are considered. We note that annotation of scientific texts for creation of training dataset is very labor insensitive and expensive process. To tackle this problem, we propose methods and tools based on active learning. We describe and

ALIBI

Application of a Hybrid Bi-LSTM-CRF Model to the Task of Russian Named Entity Recognitionn text documents into predefined categories called tags, such as person names, quantity expressions, percentage expressions, names of locations, organizations, as well as expression of time, currency and others. Although there is a number of approaches have been proposed for this task in Russian lan

宣稱

正式通知

Building Wordnet for Russian Language from Ru.Wiktionarymary concern of this study is to describe a procedure of relating words to their meanings throughout Wiktionary pages and establish synonym and hyponym-hypernym relation between specific senses of words. The produced database contains 104696 synsets and is publicly available in alpha version as a py

抱狗不敢前

Corpus of Syntactic Co-Occurrences: A Delayed Promisen the Russian language. The paper includes an overview of the corpora collected for CoSyCo creation and the amount of collected combinations. In the paper, we also provide a short evaluation of the gathered information.

大吃大喝

A Close Look at Russian Morphological Parsers: Which One Is the Best?ain tasks of morphological analysis: lemmatization and POS tagging. The experiments were conducted on three currently available Russian corpora which have qualitative morphological labeling – Russian National Corpus, OpenCorpora, and RU-EVAL (a small corpus created in 2010 to evaluate parsers). As e

外形

奇怪

悄悄移動(dòng)

Endemic Arenaviruses in Latin America,lization to increase the training speed. A fully-connected neural network reaches an average recall of 0.78, a Long Short-Term Memory neural network shows an average recall of 0.65. Advantages and disadvantages of both architectures are provided for the particular task.