In continuation of my earlier posts on designing an automated question-answering system, in part three of the series we look into how to incorporate feedback into our system. Note that since getting labelled data is an expensive operation from the perspective of our company resources, the amount of feedback from human agents is very low (~ 2-3% of the total number of questions). So obviously with such less labelled data, […]
Tag: Neural Networks
For the past few days, I have been reading quite a lot of research papers, articles and blogs related to artificial neural networks and its transition towards deep learning. With so many different methods of selecting the best neural network architecture for a problem, the optimal hyper-parameters, the best optimization algorithm and so on, it becomes a little overwhelming to connect all the dots together when we ourselves start to […]
In this post we are going to understand about Part-Of-Speech Taggers for the English Language and look at multiple methods of building a POS Tagger with the help of the Python NLTK and scikit-learn libraries. The available methods ranges from simple regular expression based taggers to classifier based (Naive Bayes, Neural Networks and Decision Trees) and then sequence model based (Hidden Markov Model, Maximum Entropy Markov Model and Conditional Random […]
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 […]
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. […]
What is the role of activation function in Neural Networks ? The role of the activation function in a neural network is to produce a non-linear decision boundary via non-linear combinations of the weighted inputs. A neural network classifier is essentially a logistic regression classifier without the hidden layers. The non-linearity to a neural network is added by the hidden layers using a sigmoid or similar activation functions.
With the recent advancements in Deep Learning and Artificial Intelligence, there has been continuous interest among machine learning enthusiasts and data scientists to explore frontiers in artificial intelligence on small to medium scale applications that was probably the realm of high speed supercomputers owned by a few tech giants only a few years ago. Few of such applications are Image and Speech Recognition, Language Translators, Automated Image Descriptions, Detecting Phrases […]