Abstract
Large text can convey various forms of sentiment information including the author’s position, positive or negative effects of some events, attitudes of mentioned entities towards to each other. In this paper, we experiment with BERT based language models for extracting sentiment attitudes between named entities. Given a mass media article and list of mentioned named entities, the task is to ex tract positive or negative attitudes between them. Efficiency of language model methods depends on the amount of training data. To enrich training data, we adopt distant supervision method, which provide automatic annotation of unlabeled texts using an additional lexical resource. The proposed approach is subdivided into two stages FRAME-BASED: (1) sentiment pairs list completion (PAIR-BASED), (2) document annotations using PAIR-BASED and FRAME-BASED factors. Being applied towards a large news collection, the method generates RuAttitudes2017 automatically annotated collection. We evaluate the approach on RuSentRel-1.0, consisted of mass media articles written in Russian. Adopting RuAttitudes2017 in the training process results in 10-13% quality improvement by F1-measure over supervised learning and by 25% over the top neural network based model results.
Highlights
В результате, 22-24% достоверных отношений из заголовков были сопоставлены с доверенными парами, среди которых 79% отношений совпадали с оценочной ориентацией соответствующих пар
Усредненная оценка вероятности внимания по головам языковой модели BERT по каждому из 12 слоев в отдельности для: токенов класса (CLS), разделителей (SEP), участникам отношения, всех сторонних токенов к FRAMES и SENTIMENT в отдельности; наибольшие значения в рядах отмечены жирным шрифтом Table 11
Nicolay Leonidovich RUSNACHENKO – PhD student of «Theoretical Informatics and Computer Technologies» (IU-9), Bauman Moscow State Technical University (BMSTU) (Moscow, Russia)
Summary
Т.е. выделение мнения автора о предмете обсуждения в тексте, является одним из наиболее востребованных приложений автоматической обработки текстов за последние годы. Применение языковых моделей в задаче извлечения оценочных отношений. Труды ИСП РАН, том 33, вып. 3, 2021 г., стр. 199-222
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