Event Extraction and Visualization for Obtaining Personal Experiences from Blogs

Nishihara, Yoko; Sato, Keita; Sunayama, Wataru

doi:10.1007/978-3-642-02559-4_35

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…This kind of structure allows us to specify the events as patterns of other events or patterns in data streams. This has been demonstrated in [19], [36]. [19] utilizes lexico-syntactic patterns related to an event to identify and extract the sentences about the event from web search results.…”

Section: Event Retrieval Frameworkmentioning

confidence: 99%

Continuous Health Interface Event Retrieval

Pandey

Nag

Jain

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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Knowing the state of our health at every moment in time is critical for advances in health science. Using data obtained outside an episodic clinical setting is the first step towards building a continuous health estimation system. In this paper, we explore a system that allows users to combine events and data streams from different sources and retrieve complex biological events, such as cardiovascular volume overload, using measured lifestyle events. These complex events, which have been explored in biomedical literature and which we call interface events, have a direct causal impact on the relevant biological systems; they are the interface through which the lifestyle events influence our health. We retrieve the interface events from existing events and data streams by encoding domain knowledge using the event operator language. The interface events can then be utilized to provide a continuous estimate of the biological variables relevant to the user's health state. The event-based framework also makes it easier to estimate which event is causally responsible for a particular change in the individual's health state.

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Section: Event Retrieval Frameworkmentioning

confidence: 99%

Continuous Health Interface Event Retrieval

Pandey

Nag

Jain

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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“…Mc-Closky et al (2011) convert text to a dependency tree and use dependency parsing to solve the problem. Aone et al (2009) and Nishihara et al (2009) focus on designed patterns to parse text. Zhou et al (2014) propose a Bayesian model to extract structured representation of events from Twitter in an unsupervised way.…”

Section: Story Event Extractionmentioning

confidence: 99%

Composing a Picture Book by Automatic Story Understanding and Visualization

Qi¹,

Song²,

Wang³

et al. 2019

Proceedings of the Second Workshop on Storytelling

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Pictures can enrich storytelling experiences. We propose a framework that can automatically compose a picture book by understanding story text and visualizing it with painting elements, i.e., characters and backgrounds. For story understanding, we extract key information from a story on both sentence level and paragraph level, including characters, scenes and actions. These concepts are organized and visualized in a way that depicts the development of a story. We collect a set of Chinese stories for children and apply our approach to compose pictures for stories. Extensive experiments are conducted towards story event extraction for visualization to demonstrate the effectiveness of our method.

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“…Several attempts have been made for extracting events using mostly manually created lexicosyntactic patterns. For example, in their 2009 work, Nishihara et al [15] proposes a system that uses lexico-syntactic patterns to split sentences in order to obtain personal experiences from blogs by visualizing an event as three kinds of pictures: action, object, and place.…”

Section: Np {} Especially {Np }*{Or | And} Npmentioning

confidence: 99%