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

[fa icon="comment"] 0 Comments

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.

Read More [fa icon="long-arrow-right"]

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

[fa icon="comment"] 0 Comments

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.

Read More [fa icon="long-arrow-right"]

On the Stanford parser (and Bitext parser)

[fa icon="calendar'] Aug 17, 2021 8:10:26 PM / by Bitext posted in Sentiment Analysis, Bitext

[fa icon="comment"] 0 Comments

In some of our recent talks, colleagues have asked us about the Stanford parser and how it compared to Bitext technology (namely at our last workshop on Semantic Analysis of Big Data in San Francisco, and in our presentation in the Semantic Garage also in San Francisco).

Read More [fa icon="long-arrow-right"]

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

[fa icon="comment"] 4 Comments

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.  

Read More [fa icon="long-arrow-right"]

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

[fa icon="comment"] 0 Comments

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.

Read More [fa icon="long-arrow-right"]

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"

[fa icon="comment"] 0 Comments

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. 
Read More [fa icon="long-arrow-right"]

Subscribe Here!