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

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11#
發(fā)表于 2025-3-23 12:26:34 | 只看該作者
A Comparative Study of Publicly Available Russian Sentiment Lexiconsdependence of their F1-measure on their TF-IDF model size. The resulting union lexicon most fully reflects the sentiment lexica of the present day Russian language and can be used both in scientific research and in applied sentiment analysis systems.
12#
發(fā)表于 2025-3-23 17:07:21 | 只看該作者
13#
發(fā)表于 2025-3-23 20:13:39 | 只看該作者
1865-0929 St. Petersburg, Russia, in October 2018. The 19 revised full papers were carefully reviewed and selected from 56 submissions and cover a wide range of topics, including morphology and word-level semantics, sentence and discourse representations, corpus linguistics, language resources, and social int
14#
發(fā)表于 2025-3-23 22:33:35 | 只看該作者
https://doi.org/10.1007/978-3-031-48129-1om LSTM. The aim is to build a model which is simple to implement, light in terms of parameters and works across multiple supervised sentence comparison tasks. We show good results for the model on two sentence comparison datasets.
15#
發(fā)表于 2025-3-24 03:25:17 | 只看該作者
Encyclopedia of Heroism Studiesa confirmation measure and an aggregation function. We designed a regularizer for topic modeling representing this score. The resulting topic modeling method shows significant superiority to all analogs in reflecting human assessments of topic interpretability.
16#
發(fā)表于 2025-3-24 07:28:19 | 只看該作者
Supervised Mover’s Distance: A Simple Model for Sentence Comparisonom LSTM. The aim is to build a model which is simple to implement, light in terms of parameters and works across multiple supervised sentence comparison tasks. We show good results for the model on two sentence comparison datasets.
17#
發(fā)表于 2025-3-24 12:24:14 | 只看該作者
Four Keys to Topic Interpretability in Topic Modelinga confirmation measure and an aggregation function. We designed a regularizer for topic modeling representing this score. The resulting topic modeling method shows significant superiority to all analogs in reflecting human assessments of topic interpretability.
18#
發(fā)表于 2025-3-24 15:23:53 | 只看該作者
Conference proceedings 2018burg, Russia, in October 2018. The 19 revised full papers were carefully reviewed and selected from 56 submissions and cover a wide range of topics, including morphology and word-level semantics, sentence and discourse representations, corpus linguistics, language resources, and social interaction a
19#
發(fā)表于 2025-3-24 19:37:43 | 只看該作者
20#
發(fā)表于 2025-3-25 01:02:15 | 只看該作者
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