Automatic Extraction of Event Information from Newspaper Articles and Web Pages

Nanba, Hidetsugu; Saito, Ryuta; Ishino, Akira; Takezawa, Toshiyuki

doi:10.1007/978-3-319-03599-4_21

Cited by 11 publications

(6 citation statements)

References 1 publication

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“…Conditional Random Field (CRF) classifier was learned to extract the artist name and location of music events from a corpus of tweets (Benson et al, 2011). Likewise, CRF applied to extract full information about events happening in all tourist spots from news articles and web pages (Nanba et al, 2013). Moreover, CRF was trained by (Ritter et al, 2012) to extract temporal expression about events.…”

Section: Event Detection Methodsmentioning

confidence: 99%

A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: Data collection, pre-processing, feature selection, event detection, performance evaluation and result representation. Among these phases, event detection phase has a vital rule in the performance of the ED model. Consequently, numerous supervised, unsupervised, semi-supervised detection methods have been introduced for this phase. However, unsupervised methods have been extensively utilized as ED process is considered as unsupervised task. Hence, such methods need to be categorized on such a way so it can help researchers to understand and identified the limitations lay in these methods. In this survey, ED models for text data from various Social Network sites (SNs) are analyzed based on domain type, detection methods, type of detection task. In addition, main categories for unsupervised detection methods are explicitly mentioned with revising their related works. Moreover, the major open challenges faced by researchers for building ED models are explained and discussed in detail. The main objective of this survey paper is to provide a complete view of the recent developments in ED field. Hence, help scholars to identify the limitations of existing ED models for text data and help them to recognize the interesting future works directions.

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Section: Event Detection Methodsmentioning

confidence: 99%

A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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“…There are many studies that have faced this problem like [10,15,17] and it is not solved yet. We have had to tackle searches over 10,000,000 documents that in some cases host large texts, such as the pages of the newspaper.…”

Section: The Prototypementioning

confidence: 95%

Knowledge Obtention Combining Information Extraction Techniques with Linked Data

Garrido

Blazquez

Buey

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

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Today, we can find a vast amount of textual information stored in proprietary data stores. The experience of searching information in these systems could be improved in a remarkable manner if we combine these private data stores with the information supplied by the Internet, merging both data sources to get new knowledge. In this paper, we propose an architecture with the goal of automatically obtaining knowledge about entities (e.g., persons, places, organizations, etc.) from a set of natural text documents, building smart data from raw data. We have tested the system in the context of the news archive of a real Media Group.

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“…It is not an easy task to extract specific information from a large number of articles with a journalistic writing style in natural language. Many studies [46]- [48] have faced such problems, which have not yet been solved. Many researchers applied events detection to a specific field [49], [50], working on detection of economic events that may influence the market, such as mergers.…”

Section: Related Workmentioning

confidence: 99%

An Artificial Intelligence Driven Multi-Feature Extraction Scheme for Big Data Detection

Wan

Zheng

et al. 2019

IEEE Access

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The Internet improves the speed of information dissemination, and the scale of unstructured text data is expanding and increasingly being used for mass communication. Although these large amounts of data meet the infinite demand, it is difficult to find public focus in a timely manner. Therefore, information extraction from big data has become an important research issue, and there are many published studies on big data processing at home and abroad. In this paper, we propose a multi-feature keyword extraction method, and based on this, an artificial intelligence driven big data MFE scheme is designed, then an application example of the general scheme is expanded and detailed. Taking news as the carrier, this scheme is applied to the algorithm design of hot event detection. As a result, a multi-feature fusion clustering algorithm is proposed based on user attention with two main stages. In the first stage, a multi-feature fusion model is developed to evaluate keywords, and this model combines the term frequency and part of speech features. We use it to extract keywords for representing news and events. In the second stage, we perform clustering and detect hot events in accordance with the procedure, and during the composition of news clusters, we analyze several variadic parameters in order to explore the optimal effectiveness. Then, experiments on the news corpus are conducted, and the results show that the approach presented herein performs well. INDEX TERMS Artificial intelligence, extraction scheme, big data, online news, news clustering.

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Automatic Extraction of Event Information from Newspaper Articles and Web Pages

Cited by 11 publications

References 1 publication

A Survey on Event Detection Models for Text Data Streams

A Survey on Event Detection Models for Text Data Streams

Knowledge Obtention Combining Information Extraction Techniques with Linked Data

An Artificial Intelligence Driven Multi-Feature Extraction Scheme for Big Data Detection

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