Exploring Random Indexing for Profile Learning

2016 ◽  
pp. 77-85
Author(s):  
Adrian Fonseca Bruzón ◽  
Aurelio López-López ◽  
José Medina Pagola
Keyword(s):  
Random Indexing

scholarly journals Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer’s disease

2019 ◽  
Vol 19 (S5) ◽  
Author(s):  
Go Eun Heo ◽  
Qing Xie ◽  
Min Song ◽  
Jeong-Hoon Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Modern Medicine ◽  
Biomedical Literature ◽  
Ranking Method ◽  
Superior Performance ◽  
Word Embeddings ◽  
Random Indexing ◽  
Meaningful Relationships

Abstract Background Extracting useful information from biomedical literature plays an important role in the development of modern medicine. In natural language processing, there have been rigorous attempts to find meaningful relationships between entities automatically by co-occurrence-based methods. It has been increasingly important to understand whether relationships exist, and if so how strong, between any two entities extracted from a large number of texts. One of the defining methods is to measure semantic similarity and relatedness between two entities. Methods We propose a hybrid ranking method that combines a co-occurrence approach considering both direct and indirect entity pair relationship with specialized word embeddings for measuring the relatedness of two entities. Results We evaluate the proposed ranking method comparatively with other well-known methods such as co-occurrence, Word2Vec, COALS (Correlated Occurrence Analog to Lexical Semantics), and random indexing by calculating top-ranked entities related to Alzheimer’s disease. In addition, we analyze gene, pathway, and gene–phenotype relationships. Overall, the proposed method tends to find more hidden relationships than the other methods. Conclusion Our proposed method is able to select more useful related entities that not only highly co-occur but also have more indirect relations for the target entity. In pathway analysis, our proposed method shows superior performance at identifying (functional) cross clustering and higher-level pathways. Our proposed method, resulting from phenotype analysis, has an advantage in identifying the common genotype relating to phenotypes from biological literature.

scholarly journals TwitterNews: Real time event detection from the Twitter data stream

2016 ◽  
Author(s):  
Mahmud Hasan ◽  
Mehmet A Orgun ◽  
Rolf Schwitter
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Detection System ◽  
Fixed Time ◽  
Streaming Data ◽  
Locality Sensitive Hashing ◽  
Vector Model ◽  
Incremental Clustering ◽  
Random Indexing ◽  
Novel Approach

Research in event detection from the Twitter streaming data has been gaining momentum in the last couple of years. Although such data is noisy and often contains misleading information, Twitter can be a rich source of information if harnessed properly. In this paper, we propose a scalable event detection system, TwitterNews, to detect and track newsworthy events in real time from Twitter. TwitterNews provides a novel approach, by combining random indexing based term vector model with locality sensitive hashing, that aids in performing incremental clustering of tweets related to various events within a fixed time. TwitterNews also incorporates an effective strategy to deal with the cluster fragmentation issue prevalent in incremental clustering. The set of candidate events generated by TwitterNews are then filtered, to report the newsworthy events along with an automatically selected representative tweet from each event cluster. Finally, we evaluate the effectiveness of TwitterNews, in terms of the recall and the precision, using a publicly available corpus.

scholarly journals A Random Indexing Approach for Web User Clustering and Web Prefetching

2012 ◽  
pp. 40-52 ◽  
Author(s):  
Miao Wan ◽  
Arne Jönsson ◽  
Cong Wang ◽  
Lixiang Li ◽  
Yixian Yang
Keyword(s):  
Web Prefetching ◽  
Random Indexing ◽  
User Clustering ◽  
Web User Clustering
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