Business Intelligence in Text Format: Are You Acting Smart?

[fa icon="calendar'] Nov 13, 2018 5:11:00 PM / by Ana Moreno posted in Text Analytics, Sentiment Analysis, Semantic Analysis

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While a picture may be worth a thousand words – a thousand words may be worth thousands of dollars. Never thought how valuable all your company unstructured data would be? This heterogeneous knowledge can turn out to be quite useful for companies, however, there is still much to be learned. 

 

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Bitext: Top Software For Text Analytics

[fa icon="calendar'] Feb 23, 2018 3:03:00 PM / by Bitext posted in Text Analytics

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Predictive Analytics Today is one of the must-read sites when it comes to provide review, comparison, research, commentary and analysis of enterprise software. It helps IT decision makers to identify the appropriate technology that fulfills their needs. The site has over 400,000 monthly users, and over 36,000 newsletter subscribers.

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Why Linguistics for Text Analytics?

[fa icon="calendar'] May 12, 2017 1:00:00 PM / by Bitext posted in Text Analytics, Machine Learning

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In previous posts, we have outlined the crucial role of Machine Learning for Analytics (in Machine Learning & Deep Linguistic Analysis in Text Analytics), and the implications of using Machine Learning for analyzing and structuring text (in What are the limitations of Machine Learning for Text Analysis?). 

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How Phrase Structure can help Machine Learning for Text Analysis

[fa icon="calendar'] Apr 28, 2017 1:00:00 PM / by Bitext posted in API, Machine Learning, Text Analytics

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This post dives into one of the topics of a previous post "Machine Learning & Deep Linguistic Analysis in Text Analytics". We referred to the strong points of Machine Learning technology for insight extraction. We also stated that using variations of classical "bag of words" models limits the ability of Machine Learning to extract insights. Here we go into some detail on this last statement.

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Machine Learning & Deep Linguistic Analysis in Text Analytics

[fa icon="calendar'] Mar 24, 2017 4:01:23 PM / by Bitext posted in Machine Learning, Text Analytics

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Text analysis is becoming a pervasive task in many business areas. Machine Learning is the most common approach used in text analysis, and is based on statistical and mathematical models. Linguistic approaches, which are based on knowledge of language and its structure, are far less frequently used. These two approaches are often seen as alternative or competing approaches.

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