scholarly journals Prior Knowledge-Based Event Network for Chinese Text

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yunyu Shi ◽  
Jianfang Shan ◽  
Xiang Liu ◽  
Yongxiang Xia
Keyword(s):  
Prior Knowledge ◽  
Chinese Text ◽  
Large Scale ◽  
Text Processing ◽  
Event Extraction ◽  
Knowledge Based ◽  
Text Understanding ◽  
Text Information ◽  
Data Source ◽  
Lexical Relations

Text representation is a basic issue of text information processing and event plays an important role in text understanding; both attract the attention of scholars. The event network conceals lexical relations in events, and its edges express logical relations between events in document. However, the events and relations are extracted from event-annotated text, which makes it hard for large-scale text automatic processing. In the paper, with expanded CEC (Chinese Event Corpus) as data source, prior knowledge of manifestation rules of event and relation as the guide, we propose an event extraction method based on knowledge-based rule of event manifestation, to achieve automatic building and improve text processing performance of event network.

scholarly journals Event Extraction and Representation: A Case Study for the Portuguese Language

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 205 ◽  
Author(s):  
Paulo Quaresma ◽  
Vítor Beires Nogueira ◽  
Kashyap Raiyani ◽  
Roy Bayot
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Event Extraction ◽  
Semantic Role Labeling ◽  
Knowledge Based ◽  
Part Of Speech ◽  
Relevant Role ◽  
Text Information ◽  
Future Work

Text information extraction is an important natural language processing (NLP) task, which aims to automatically identify, extract, and represent information from text. In this context, event extraction plays a relevant role, allowing actions, agents, objects, places, and time periods to be identified and represented. The extracted information can be represented by specialized ontologies, supporting knowledge-based reasoning and inference processes. In this work, we will describe, in detail, our proposal for event extraction from Portuguese documents. The proposed approach is based on a pipeline of specialized natural language processing tools; namely, a part-of-speech tagger, a named entities recognizer, a dependency parser, semantic role labeling, and a knowledge extraction module. The architecture is language-independent, but its modules are language-dependent and can be built using adequate AI (i.e., rule-based or machine learning) methodologies. The developed system was evaluated with a corpus of Portuguese texts and the obtained results are presented and analysed. The current limitations and future work are discussed in detail.

scholarly journals Measure Term Similarity using a Semantic Network Approach

2021 ◽  
pp. 5-9
Author(s):  
D. M. Kulkarni ◽  
◽  
Swapnaja S. Kulkarni ◽  
Keyword(s):  
Semantic Similarity ◽  
Search Engines ◽  
Large Scale ◽  
Semantic Network ◽  
Network Approach ◽  
Traditional System ◽  
Knowledge Based ◽  
Text Understanding ◽  
Large Scale Dataset ◽  
Term Similarity

Computing semantic similarity between two words comes with variety of approaches. This is mainly essential for the applications such as text analysis, text understanding. In traditional system search engines are used to compute the similarity between words. In that search engines are keyword based. There is one drawback that user should know what exactly they are looking for. There are mainly two main approaches for computation namely knowledge based and corpus based approaches. But there is one drawback that these two approaches are not suitable for computing similarity between multi-word expressions. This system provides efficient and effective approach for computing term similarity using semantic network approach. A clustering approach is used in order to improve the accuracy of the semantic similarity. This approach is more efficient than other computing algorithms. This technique can also apply to large scale dataset to compute term similarity.

scholarly journals An Improved Random Walker with Bayes Model for Volumetric Medical Image Segmentation

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Chunhua Dong ◽  
Xiangyan Zeng ◽  
Lanfen Lin ◽  
Hongjie Hu ◽  
Xianhua Han ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Random Walk ◽  
Prior Knowledge ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Segmentation ◽  
Shape Information ◽  
Seed Point ◽  
Random Walker ◽  
Knowledge Based

Random walk (RW) method has been widely used to segment the organ in the volumetric medical image. However, it leads to a very large-scale graph due to a number of nodes equal to a voxel number and inaccurate segmentation because of the unavailability of appropriate initial seed point setting. In addition, the classical RW algorithm was designed for a user to mark a few pixels with an arbitrary number of labels, regardless of the intensity and shape information of the organ. Hence, we propose a prior knowledge-based Bayes random walk framework to segment the volumetric medical image in a slice-by-slice manner. Our strategy is to employ the previous segmented slice to obtain the shape and intensity knowledge of the target organ for the adjacent slice. According to the prior knowledge, the object/background seed points can be dynamically updated for the adjacent slice by combining the narrow band threshold (NBT) method and the organ model with a Gaussian process. Finally, a high-quality image segmentation result can be automatically achieved using Bayes RW algorithm. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for liver segmentation (p<0.001).

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

scholarly journals Applying the Bell’s Test to Chinese Texts

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 275
Author(s):  
Igor A. Bessmertny ◽  
Xiaoxi Huang ◽  
Aleksei V. Platonov ◽  
Chuqiao Yu ◽  
Julia A. Koroleva
Keyword(s):  
Quantum Entanglement ◽  
Chinese Text ◽  
Search Engines ◽  
Text Processing ◽  
Word Segmentation ◽  
Significant Problem ◽  
Text Segmentation ◽  
Text Documents ◽  
Segmentation Algorithms ◽  
Chinese Texts

Search engines are able to find documents containing patterns from a query. This approach can be used for alphabetic languages such as English. However, Chinese is highly dependent on context. The significant problem of Chinese text processing is the missing blanks between words, so it is necessary to segment the text to words before any other action. Algorithms for Chinese text segmentation should consider context; that is, the word segmentation process depends on other ideograms. As the existing segmentation algorithms are imperfect, we have considered an approach to build the context from all possible n-grams surrounding the query words. This paper proposes a quantum-inspired approach to rank Chinese text documents by their relevancy to the query. Particularly, this approach uses Bell’s test, which measures the quantum entanglement of two words within the context. The contexts of words are built using the hyperspace analogue to language (HAL) algorithm. Experiments fulfilled in three domains demonstrated that the proposed approach provides acceptable results.

scholarly journals Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

2017 ◽  
Vol 221 ◽  
pp. 427-436 ◽  
Author(s):  
Anthony L. Schroeder ◽  
Dalma Martinović-Weigelt ◽  
Gerald T. Ankley ◽  
Kathy E. Lee ◽  
Natalia Garcia-Reyero ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
River Basin ◽  
Biological Effects ◽  
Knowledge Based

A noise reduction method for semi-supervised community detection based on harmonic function

2018 ◽  
Vol 32 (14) ◽  
pp. 1850166 ◽  
Author(s):  
Lilin Fan ◽  
Kaiyuan Song ◽  
Dong Liu
Keyword(s):  
Harmonic Function ◽  
Prior Knowledge ◽  
Community Detection ◽  
Detection Methods ◽  
Detection Process ◽  
Knowledge Based ◽  
Detection Approach ◽  
Series Of Experiments ◽  
Novel Strategy ◽  
The Impact

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved.

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