Approximate entity extraction in temporal databases

2011 ◽  
Vol 14 (2) ◽  
pp. 157-186 ◽  
Author(s):  
Wei Lu ◽  
Gabriel Pui Cheong Fung ◽  
Xiaoyong Du ◽  
Xiaofang Zhou ◽  
Lijiang Chen ◽  
...  
Keyword(s):  
Temporal Databases ◽  
Entity Extraction ◽  
Approximate Entity Extraction

scholarly journals Improving Topic Coherence Using Entity Extraction Denoising

2018 ◽  
Vol 110 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Ronald Cardenas ◽  
Kevin Bello ◽  
Alberto Coronado ◽  
Elizabeth Villota
Keyword(s):  
Topic Modeling ◽  
Human Perception ◽  
Relevant Information ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Fine Grained ◽  
Job Advertisement ◽  
Coherence Score ◽  
Probabilistic Topic Modeling ◽  
Promising Solution

Abstract Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method and the evaluation of this model is an interesting problem on its own. Topic interpretability measures have been developed in recent years as a more natural option for topic quality evaluation, emulating human perception of coherence with word sets correlation scores. In this paper, we show experimental evidence of the improvement of topic coherence score by restricting the training corpus to that of relevant information in the document obtained by Entity Recognition. We experiment with job advertisement data and find that with this approach topic models improve interpretability in about 40 percentage points on average. Our analysis reveals as well that using the extracted text chunks, some redundant topics are joined while others are split into more skill-specific topics. Fine-grained topics observed in models using the whole text are preserved.

scholarly journals Entity Extraction of Electrical Equipment Malfunction Text by a Hybrid Natural Language Processing Algorithm

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 40216-40226
Author(s):  
Zhe Kong ◽  
Changxi Yue ◽  
Ying Shi ◽  
Jicheng Yu ◽  
Changjun Xie ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Electrical Equipment ◽  
Processing Algorithm ◽  
Entity Extraction ◽  
Natural Language Processing Algorithm

scholarly journals Cache-efficient sweeping-based interval joins for extended Allen relation predicates

2021 ◽  
Author(s):  
Danila Piatov ◽  
Sven Helmer ◽  
Anton Dignös ◽  
Fabio Persia
Keyword(s):  
Data Structure ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Temporal Databases ◽  
Access Method ◽  
Wide Range ◽  
Interval Relation ◽  
Cache Efficient ◽  
Join Algorithms ◽  
Better Than

AbstractWe develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

Main Memory-Based Algorithms for Efficient Parallel Aggregation for Temporal Databases

2004 ◽  
Vol 16 (2) ◽  
pp. 123-163 ◽  
Author(s):  
Dengfeng Gao ◽  
Jose Alvin G. Gendrano ◽  
Bongki Moon ◽  
Richard T. Snodgrass ◽  
Minseok Park ◽  
...  
Keyword(s):  
Main Memory ◽  
Temporal Databases
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