Machine Learning, AI and Programming

Tag: Word2Vec

Designing an Automated Question-Answering System - Part I

Natural Language Question Answering system such as chatbots and AI conversational agents requires answering customer queries in an intelligent fashion. Many companies employ manual resources to answer customer queries and complaints. Apart from the high cost factor with employing people, many of the customer queries are repetitive in nature and most of the time, same intents are asked in different tones.

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Machine Learning Interview Questions and Answers (Part II)

Why does negative sampling strategy works during training of word vectors ? In word2vec training the objective is to have semantically and syntactically similar words close to each other in terms of the cosine distance between their word vectors. In the skip-gram architecture, the probability of a word 'c' being predicted as a context word at the output node, given the target word 'w' and the input and output weights […]

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Generative vs. Discriminative Spell Corrector

We have earlier seen two approaches of doing spelling corrections in text documents. Most of the spelling errors encountered are in either user generated contents or OCR outputs of document images. Presence of spelling errors introduce noise in data and as a result impact of important features gets diluted. Although the methods explained are  different in how they are implemented but theoretically both of them work on the same principle. […]

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Designing a Contextual Graphical Model for Words

I have been reading about Word Embedding methods that encode words found in text documents into multi-dimensional vectors. The purpose of encoding into vectors is to give "meaning" to words or phrases in a context. Traditional methods of document classification treat each word in isolation or at-most use a N-gram approach i.e. in vector space, the words are represented as one-hot vectors which are sparse and do not convey any meaning whereas […]

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Understanding Word Vectors and Word2Vec

Quite recently I have been exploring the Word2Vec tool, for representing words in text documents as vectors. I got the initial ideas about word2vec utility from Google's code archive webpage. The idea behind coming up with this kind of utility caught my interest and later I went on to read the following papers by Mikolov et. al. to better understand the algorithm and its implementation. Efficient Estimation of Word Representations […]

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