IBM Watson is one of the most prominent Natural Language Processing tools that supports information retrieval via question answering. Watson is guiding us with decision-making in literally any domain, such as weather, healthcare, insurance, banking, media and more. Let us see how IBM Watson works through an example.
We will analyze how Watson NLP works using the demo available on the IBM website.
Currently, Watson provides text analysis against seven parameters as shown above – Sentiment, Emotion, Keywords, Entities, Categories, Concept, and Sematic roles. We are sharing two of the results for your reference:
Stanford NLP provides human language technology tools that can provide the base forms of words and their parts of speech. It can identify whether the words are names of companies or people. Stanford NLP is an integrated NLP toolkit with a wide range of grammatical analysis tools. It supports a number of human languages and supports high quality text analytics. Stanford can be run as a simple web service and APIs are available for most of the latest programming languages.
We will analyze how Stanford NLP works using the demo available on their website.
We are sharing two of the results for your reference.
Note: Stanford’s Part-of-speech (POS) tagger and Co-reference resolution system are not available in IBM Watson.