5,730 research outputs found
Event detection in field sports video using audio-visual features and a support vector machine
In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable
Developing Intelligent Assistants to Search for Content on Websites of a Certain Genre
This paper discusses an approach to automatic generation of intelligent assistants, which provide information search on the content of a website. A feature of the approach is to use genre models, developed for a given type of resource (educational, informational, etc.), on the basis of which the genre structuring and subsequent thematic clustering of the content of the target website is performed. The resulting genre structures allow us to define more precisely the boundaries of thematic clusters related to the topic of the user’s search query. The search quality evaluation for the Russian-language websites showed an F-score of 87.8% and originality of 80.9%, which exceeds the Yandex search engine results by 1.1% and 9.1%, respectively. In order to predict user information needs, a method for refining the resulting sample is proposed. It allows a user to get information implicitly, based on current and previous queries, about what the user was not satisfied with in the previous search results. A model of user’s search intentions has been developed and its computational component includes a method for evaluating query closeness based on the FRiS function. Based on the proposed methods, a chatbot was created on the Telegram messenger platform to search the websites of educational institutions. The experiments showed that the user needs the average of 1.75 qualifying questions to find the necessary information.This paper discusses an approach to automatic generation of intelligent assistants, which provide information search on the content of a website. A feature of the approach is to use genre models, developed for a given type of resource (educational, informational, etc.), on the basis of which the genre structuring and subsequent thematic clustering of the content of the target website is performed. The resulting genre structures allow us to define more precisely the boundaries of thematic clusters related to the topic of the user’s search query. The search quality evaluation for the Russian-language websites showed an F-score of 87.8% and originality of 80.9%, which exceeds the Yandex search engine results by 1.1% and 9.1%, respectively. In order to predict user information needs, a method for refining the resulting sample is proposed. It allows a user to get information implicitly, based on current and previous queries, about what the user was not satisfied with in the previous search results. A model of user’s search intentions has been developed and its computational component includes a method for evaluating query closeness based on the FRiS function. Based on the proposed methods, a chatbot was created on the Telegram messenger platform to search the websites of educational institutions. The experiments showed that the user needs the average of 1.75 qualifying questions to find the necessary information
Multi-Task Attentive Residual Networks for Argument Mining
We explore the use of residual networks and neural attention for argument
mining and in particular link prediction. The method we propose makes no
assumptions on document or argument structure. We propose a residual
architecture that exploits attention, multi-task learning, and makes use of
ensemble. We evaluate it on a challenging data set consisting of user-generated
comments, as well as on two other datasets consisting of scientific
publications. On the user-generated content dataset, our model outperforms
state-of-the-art methods that rely on domain knowledge. On the scientific
literature datasets it achieves results comparable to those yielded by
BERT-based approaches but with a much smaller model size.Comment: 12 pages, 2 figures, submitted to IEEE Transactions on Neural
Networks and Learning System
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