Machine learning
Model Training
Our Machine Learning services involve creating models using both supervised and unsupervised algorithms.
When using supervised learning, we train models using the input parameters, called features, and the outputs for each set of features. During this training, the model adjusts its function to match data. This way, the model is provided with the ability to automatically learn from the environment and apply that learning to make better decisions.
Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, speech recognition, translating languages, and making decisions. We use deep learning algorithms to train models for natural language processing systems for both extractive and abstractive text summarization. Deep learning uses multiple layers to process features, where each layer extracts only a specific piece of valuable information.
NLP Techniques
01
Lemmatization and stemming
Stemming and lemmatization are the first two steps to build an NLP project. They represent the field’s core concepts and are often the first techniques you will implement on your journey to be an NLP master.
02
Keywords extraction
Keyword extraction is an NLP technique used for text analysis. It is often used as a first step to summarize the main ideas of a text and to deliver the key ideas presented in the text.
03
Named Entity Recognition (NER)
NER is a technique used to extract entities from a body of a text used to identify basic concepts within the text, such as people’s names, places, dates, etc.
04
Topic Modelling
Multiple algorithms can be used to model a topic of text, such as Correlated Topic Model, Latent Dirichlet Allocation, and Latent Sentiment Analysis. The most commonly used approach is the Latent Dirichlet.
05
Summarization
Text summarization is the process of reducing a large body of text into a smaller chuck containing the text’s main message. This technique is often used in long news articles and to summarize research papers.
06
Sentiment Analysis
Sentiment analysis can be implemented using either supervised or unsupervised techniques. We use a supervised technique called Naive Bayes algorithm to perform sentiment analysis.