Why Linguistics for Text Analysis?

[fa icon="calendar'] Sep 17, 2021 9:00:00 AM / by Bitext posted in Machine Learning, Bitext, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, ml

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In previous posts, we have outlined the crucial role of Machine Learning for Analytics (in How to Make Machine Learning more Effective using Linguistic Analysis?), and the implications of using Machine Learning for analyzing and structuring text (in How Phrase Structure helps Machine Learning?).  In a following post, we will explain how Linguistics can complement Machine Learning and how it can be integrated in the same technology stack.

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How Phrase Structure helps Machine Learning

[fa icon="calendar'] Sep 10, 2021 4:08:01 PM / by Bitext posted in Machine Learning, Bitext, Text Analytics, Text Categorization, Chatbots, bot methodology

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This post dives into one of the topics of a previous post "How to Make Machine Learning more effective using Linguistic Analysis". We referred to the strong points of Machine Learning technology for insight extraction. We also stated that text analysis is not the area where machine learning shines the most. Here we go into some detail on this last statement.

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How to Make Machine Learning more Effective using Linguistic Analysis

[fa icon="calendar'] Sep 3, 2021 3:58:29 PM / by Bitext posted in Machine Learning, Text Analytics, Artificial Intelligence, Chatbots, Conversational AI

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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. 

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What is the difference between stemming and lemmatization?

[fa icon="calendar'] Jul 7, 2021 8:54:10 PM / by Bitext posted in Machine Learning, NLP, Bitext, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, Stemming, AI, Multilanguage, Lemmatization, NLP for Core, NLP for Chatbots, Conversational AI

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Stemming and lemmatization are methods used by search engines and chatbots to analyze the meaning behind a word. Stemming uses the stem of the word, while lemmatization uses the context in which the word is being used. We'll later go into more detailed explanations and examples.  

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What do you evaluate in your chatbots? Some ideas

[fa icon="calendar'] May 31, 2021 10:00:00 AM / by Bitext posted in Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, NLU, POS tagging, AI, Multilanguage, NLP for Core, NLP for Chatbots, "Multilingual synthetic data"

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In this blog we will discuss three ways of doing your chatbot evaluation by using:

  1. real world evaluation data
  2. synthetic data
  3. "in scope" or "out of scope" queries
You have a chatbot up and running, offering help to your customers. But how do you know whether the help you are providing is correct or not?  Chatbot evaluation can be complex, especially because it is affected by many factors. 
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Noisy text is realistic text

[fa icon="calendar'] Feb 24, 2020 4:45:00 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, NLG, NLU, Query Rewriting, AI, Multilanguage, NLP for Core, NLP for Chatbots, NLP for CX, "Multilingual synthetic data"

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One of the flaws of usual training data generation is that, when you ask somebody to manually create training data for you, they will make an effort to write these sentences correctly, following the spelling and punctuation norms of your language. Even if some errors appear, they will be minimal, because they are trying to do things right —this is, to provide “orthographically right” sentences.

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