scholarly journals A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text

2021 ◽  
Author(s):  
Andrea Seveso ◽  
Fabio Mercorio ◽  
Mario Mezzanzanica
Keyword(s):  
Semantic Relations ◽  
Methodological Framework ◽  
Text Data ◽  
Human In The Loop ◽  
Unstructured Text ◽  
Learning From Text ◽  
Structured Representation

Taxonomies provide a structured representation of semantic relations between lexical terms, acting as the backbone of many applications. The research proposed herein addresses the topic of taxonomy enrichment using an ”human-in-the-loop” semi-supervised approach. I will be investigating possible ways to extend and enrich a taxonomy using corpora of unstructured text data. The objective is to develop a methodological framework potentially applicable to any domain.

The latent learning model to derive semantic relations of words from unstructured text data in social media

2018 ◽  
Vol 78 (20) ◽  
pp. 28649-28663
Author(s):  
Jiwan Seo ◽  
Karam Yoo ◽  
Seungjin Choi ◽  
Yura Alex Kim ◽  
Sangyong Han
Keyword(s):  
Social Media ◽  
Learning Model ◽  
Semantic Relations ◽  
Latent Learning ◽  
Text Data ◽  
Unstructured Text

Information Extraction

2015 ◽  
Author(s):  
Ralph Grishman
Keyword(s):  
Knowledge Representation ◽  
Information Extraction ◽  
Semantic Relations ◽  
Supervised Training ◽  
Unstructured Text ◽  
Structured Knowledge

Information extraction constructs a structured knowledge representation from unstructured text, so that the knowledge may be further used for search, inference, and analysis. Given a specification of select types of entities, semantic relations, and events, it builds a database from instances of this information in text. This chapter describes the stages of processing involved and considers how such systems may be built using hand-coded rules, supervised training, and semi-supervised training.

Incorporating Text OLAP in Business Intelligence

2011 ◽  
pp. 77-101 ◽  
Author(s):  
Byung-Kwon Park ◽  
Il-Yeol Song
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Business Intelligence ◽  
Multidimensional Analysis ◽  
Web Pages ◽  
Data Types ◽  
Text Documents ◽  
Text Data ◽  
Platform Architecture ◽  
Unstructured Text

As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both data types for total business intelligence. The data can be classified into two categories: structured and unstructured. For getting total business intelligence, it is important to seamlessly analyze both of them. Especially, as most of business data are unstructured text documents, including the Web pages in Internet, we need a Text OLAP solution to perform multidimensional analysis of text documents in the same way as structured relational data. We first survey the representative works selected for demonstrating how the technologies of text mining and information retrieval can be applied for multidimensional analysis of text documents, because they are major technologies handling text data. And then, we survey the representative works selected for demonstrating how we can associate and consolidate both unstructured text documents and structured relation data for obtaining total business intelligence. Finally, we present a future business intelligence platform architecture as well as related research topics. We expect the proposed total heterogeneous business intelligence architecture, which integrates information retrieval, text mining, and information extraction technologies all together, including relational OLAP technologies, would make a better platform toward total business intelligence.

Text-Driven Reasoning and Multi-Structured Data Analytics for Business Intelligence

2016 ◽  
pp. 1-32 ◽  
Author(s):  
Lipika Dey ◽  
Ishan Verma
Keyword(s):  
Business Intelligence ◽  
Data Analytics ◽  
Statistical Significance ◽  
Heterogeneous Data ◽  
Structured Data ◽  
Text Data ◽  
Business Operations ◽  
Unstructured Text ◽  
Business Data ◽  
Heterogeneous Resources

Business Intelligence (BI) refers to an organization's capability to gather and analyze data about business operations and transactions in order to evaluate its performance. The abundance of information both within the enterprise and outside of it has necessitated a change in traditional Business Intelligence practices. There is a need to exploit heterogeneous resources. Text data like news, analyst reports, etc. helps in better interpretation of business data. In this chapter, the authors present a futuristic BI framework that facilitates acquisition, indexing, and analysis of heterogeneous data for extracting business intelligence. It enables integration of unstructured text data and structured business data seamlessly to generate insights. The authors propose methods that can help in extraction of events or significant happenings from both unstructured and structured data, correlate the events, and thereafter reason to generate insights. The insights extracted could be validated as cause-effect pairs based on the statistical significance of co-occurrence of events.

scholarly journals Modelling the sensory space of varietal wines: Mining of large, unstructured text data and visualisation of style patterns

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Carlo C. Valente ◽  
Florian F. Bauer ◽  
Fritz Venter ◽  
Bruce Watson ◽  
Hélène H. Nieuwoudt
Keyword(s):  
Text Data ◽  
Unstructured Text ◽  
Sensory Space

scholarly journals Effective processing of unstructured data using python in Hadoop map reduce

2018 ◽  
Vol 7 (2.21) ◽  
pp. 417
Author(s):  
K Kousalya ◽  
Shaik Javed Parvez
Keyword(s):  
Open Source ◽  
Unstructured Data ◽  
Map Reduce ◽  
Text Data ◽  
Apache Hadoop ◽  
Unstructured Text ◽  
Wide Range ◽  
Two Stages

In present scenario, the growing data are naturally unstructured. In this case to handle the wide range of data, is difficult. The proposed paper is to process the unstructured text data effectively in Hadoop map reduce using Python. Apache Hadoop is an open source platform and it widely uses Map Reduce framework. Map Reduce is popular and effective for processing the unstructured data in parallel manner.  There are two stages in map reduce, namely transform and repository. Here the input splits into small blocks and worker node process individual blocks in parallel. This map reduce generally is based on java. While Hadoop Streaming allows writing mapper and reducer in other languages like Python. In this paper, we are going to show an alternative way of processing the growing unstructured content data by using python. We will also compare the performance between java based and non-java based programs. 

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