Tools to crack your data science Interviews
The HMM is a latent variable model where the observed sequence of variables are assumed to be generated from a set of temporally connected latent variables . The joint distribution of the observed variables or data and the latent variables can be written as : One possible interpretation of the latent variables in…
An n-grams model makes order n-1 Markov assumption. This assumption implies: given the previous n-1 words, probability of word is independent of words prior to words. Suppose we have k words in a sentence, their joint probability can be expressed as follows using chain rule: Now, the Markov assumption can be used to make…
Language models can be built using the following popular methods – Using n-gram language model n-gram language models make assumption for the value of n. Larger the value of n, longer the dependency. One can refer to what is the significance of n-grams in a language model for further reading. Using hidden Markov Model(HMM) HMM maintains long…
While building language model, we try to estimate the probability of the sentence or a document. Given sequences(sentences or documents) like Language model(bigram language model) will be : for each sequence given by above equation. Once we apply Maximum Likelihood Estimation(MLE), we should have a value for the term . Perplexity…
A language model is a probability distribution over sequences of words P(w_1,… ,w_m). It enables us to measure the relative likelihood of different phrases. Measuring the likelihood of a sequence of words is useful in many NLP tasks such as speech recognition, machine translation, POS tagging, parsing, and so on. Example : In any generative…
Auto Suggestion feature involves recommending the next word in a sentence or a phrase. For this, we need to build a language model on large enough corpus of “relevant” data. There are 2 caveats here – large corpus because we need to cover almost every case. This is important for recall. relevant data is useful…