How to use Word Embeddings in real-life (Part I)

[fa icon="calendar'] Sep 27, 2021 9:00:00 AM / by Bitext posted in Machine Learning, ml, ml algorithms, Word Embeddings, algorithms, computational

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While a lot of research has been devoted to analyzing the theoretical basis of word embeddings, not as much effort has gone towards examining the limitations of using them in production environments

This article is the first of a series about word embeddings as the basis for user-facing text analysis applications.

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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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Evaluate the Quality of your Chatbots and Conversational Agents

[fa icon="calendar'] Jun 10, 2021 4:07:00 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, Phrase Extraction, NLG, TechCrunch, NLU, AI, Multilanguage, NLP for Core, NLP for Chatbots

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It is always important to evaluate the quality of your chatbots and conversational agents in order to know the its real health, accuracy and efficiency. Chatbot accuracy can only be increased by constantly evaluating and retraining it with new data that answers your customer's queries. 

Chatbots require large amounts of training data to perform correctly. If you want your chatbot to recognize a specific intent, you need to provide a large number of sentences that express that intent, usually generated by hand. This manual generation is error-prone and can cause erroneous results.

How can we solve it?

With artificially-generated data. Since Dialogflow is one of the most popular chatbot-building platforms, we chose to perform our tests using it.

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