Problems As the vocabulary size (V) is large, these vectors will be large in size. They will be sparse as a word may not have co-occurred with all possible words. Resolution Dimensionality Reduction using approaches like Singular Value Decomposition (SVD) of the term document matrix to get a K dimensional approximation. Other Matrix factorisation techniques…

# Category: Natural Language Processing

## What is negative sampling when training the skip-gram model ?

Recap: Skip-Gram model is a popular algorithm to train word embeddings such as word2vec. It tries to represent each word in a large text as a lower dimensional vector in a space of K dimensions such that similar words are closer to each other. This is achieved by training a feed-forward network where we try…

## What is PMI ?

PMI : Pointwise Mutual Information, is a measure of correlation between two events x and y. As you can see from above expression, is directly proportional to the number of times both events occur together and inversely proportional to the individual counts which are in the denominator. This expression ensures high…

## Given the following two sentences, how do you determine if Teddy is a person or not? “Teddy bears are on sale!” and “Teddy Roosevelt was a great President!”

This is an example of Named Entity Recognition(NER) problem. One can build a sequence model such as an LSTM to perform this task. However, as shown in both the sentences above, forward only LSTM might fail here. Using forward only direction LSTM might result in a model which recognises Teddy as a product : “bear”, which is on…

## Say you’ve generated a language model using Bag of Words (BoW) with 1-hot encoding , and your training set has lot of sentences with the word “good” but none with the word “great”. Suppose I see sentence “Have a great day” p(great)=0.0 using this language model. How can you solve this problem leveraging the fact that good and great are similar words?

BoW with 1-hot encoding doesn’t capture the meaning of sentences, it only captures co-occurrence statistics. We need to build the language model using features which are representative of the meaning of the words. A simple solution could be to cluster the word embeddings and group synonyms into a unique token. Alternately, when a word has…

## Suppose you are modeling text with a HMM, What is the complexity of finding most the probable sequence of tags or states from a sequence of text using brute force algorithm?

Assume there are total states and let be the length of the largest sequence. Think how we generate text using an hMM. We first have a state sequence and from each state we emit an output. From each state, any word out of possible outcomes can be generated. Since there are states, at each possible…

## How do you find the most probable sequence of POS tags from a sequence of text?

This problem can be solved with a HMM. Using a HMM involves finding the transition probabilities (what is the probability of going from one POS tag to another and emission/output probabilities (what is the probability of observing a word given a POS tag) as explained in the question How do you train an hMM. Once…

## How do you train a hMM model in practice ?

The joint probability distribution for the HMM model is given by the following equation where are the observed data points and the corresponding latent states: Before proceeding to answer the question on training a HMM, it makes sense to ask following questions What is the problem in hand for which we are training…

## What are the different independence assumptions in hMM & Naive Bayes ?

Both the hMM and Naive Bayes have conditional independence assumption. hMM can be expressed by the equation below : Second equation implies a conditional independence assumption: Given the state observed variable is conditionally independent of previous observed variables, i.e. and Naive Bayes Model is expressed as: is the feature…

## How many parameters are there for an hMM model?

Let us calculate the number of parameters for bi-gram hMM given as Let be the total number of states and be the vocabulary size and be the length of the sequence Before directly estimating the number of parameters, let us first try to see what are the different probabilities or rather probability matrix…