How question answering systems work, from information retrieval based pipelines to modern neural approaches in Natural Language Processing. Question answering systems aim to respond to natural language questions with precise answers rather than long lists of documents. Instead of simply…
How machine translation evolved from rule based and statistical methods to modern neural MT with attention. Automatic translation of human languages is one of the oldest and most ambitious goals in Natural Language Processing. Decades before deep learning, researchers tried…
Explore information extraction, named entity recognition and relation extraction using dependency and constituency grammars. Most text on the web is unstructured. Articles, news, emails and social media posts are easy for humans to read but difficult for machines to query…
Understand information retrieval basics, vector space model, cosine similarity and evaluation using precision and recall. Search is one of the most visible applications of Natural Language Processing. Whenever a user types “best deep learning tutorials”, “POS tagging example in Python”…
Text classification is one of the most practical skills in Natural Language Processing. Whenever we automatically categorise emails into spam and non spam, label product reviews as positive or negative, or route support tickets to the right department, we are…
Natural language is deeply tied to time and to real usage in communities. When people describe events, they specify when something happened, how long it lasted and how different events relate in time. At the same time, they learn and…