Wouldn’t it be an amazing experience when we actually see computers start speaking the language of sages and chant Sanskrit hymns in the clearest of tone and context? Or, a computer starts painting like Leonardo Di Vinci, or start writing like Egyptians! Well, the day is not far, as we are doing some remarkable work in the fields of Natural Language Processing and Programming, assisted ably by other Artificial Intelligence capabilities such as Computer Vision (CV) and conversational AI. Well, a majority of projects assigned in the data science course align to the modern requirements of NLP designing and development, allowing some kind of embedded AI that makes training computers on human simulation so much more interesting.
In this article, I have pointed to embedding techniques in data science that every developer should be aware of and start coding with to create the next level of NLP applications for the future.
Term Frequency-Inverse Document Frequency
Also referred to as TF-IDF, this is a powerful statistical weighing model that is used in text mining and document processing. Advancement in text analytics with AI courses has allowed data scientists to extract the correct meaning of any sentence, paragraph, or blog. It is particularly useful in the decryption of codes that use symbols. For instance, TF-IDF would accurately project the nearest possible meaning based on the number of times a particular word, symbol or phrase has occurred in the piece of information. Ranking function with summarization and classification of the search engine or recommendation engines is just the precursor component of advanced embedded AI systems for document processors.
What I can use with TF-IDF?
You can use multiple statistical models with the TF-IDF. In an AI course, you can experiment with concepts of word embedding that are so commonly embedded with neural networks, syntactic parsing, and sentiment analysis.
TF-IDF embedding AI can also be applied to advanced fields of applied mathematics, fluid mechanics, neuroscience, and bioinformatics.
Latent Semantic Analysis or LSA
LSA is another NLP technique that forms the crux of distributional semantics. LSA can be embedded with new form vectors such as Google-backed Word2Vec that can analyze the exact relationship between the words and phrases when used together. Developers can vector measurement techniques to normalize the degree of dependence or independence between contextually similar words and phrases, and therefore enable NLP models to go a yard ahead with time prediction and composition.
If you want to learn LSA, you would have to expand your knowledge on singular vector decomposition or SVD, which would allow further improvement of the effectiveness of an NLP model for text-document classification.
Some common applications of LSA based embedded AI are as follows:
• Spam filtering
• Document attribution/ Authorship
• Dream analysis / psychology
• Social networking content relationship / Fake news detection
• Review / recommendation analysis management
• Automatic Hashtag and link generation, media intelligence
As we head into the next era with a data science course on text analytics, we should be prepared for more vector-based NLP frameworks.