Auto Correct is generally used to provide suggestion on mobile while typing and
Auto Correct Spelling Preprocess data and compute word probabilities from corpus Generate words 1 and 2 edit distance away and filter based on vocabulary Suggest word with highest probabilities Auto complete is utilized to complete search query or while writing email.
Preprocessed/encoded documents into vector for entire corpus Implemented local sensitive hashing(LSH) for multiple universe (different set of random planes) Developed document search using approximate k-nearest neighbor and LSH
Dataset : E2E NLG Challenge
Each instance consist of a dialogue act-based meaning representation (MR) and up to 5 references in natural language
MR:
name[The Eagle], eatType[coffee shop], food[French], priceRange[moderate], customerRating[3/5], area[riverside], kidsFriendly[yes], near[Burger King] NL:
Named Entity Recognition is a method to locate and identify important concepts within documents. I trained vanilla and GRU variant of recurrent neural network to identify person,location and organization from sentences.
Parts of speech tagging the process of assigning a part-of-speech tag (Noun, Verb, Adjective…) to each word in an input text. I have trained two different model HMM which is generative model and MEMM which is discriminative.
Implement cbow model to get word embedding with 2 different architecture First approach Bengio et al. neural language model Second approach Efficient Estimation of Word Representations in Vector Space Perform PCA on word vectors and visualize relation between few words