Natural Language Processing Engineer

B) Convolutional Neural Networks

CNNs are popular choice for text classification problems because they take into consideration left and right contexts of the words as features which can solve the problem of polysemy

A) Compare the dictionary definition of an ambiguous word with the terms contained in its neighborhood

Option 1 is called Lesk algorithm, used for word sense disambiguation, rest others cannot be used.

C) Skip-gram is a deep neural network model

Word2vec contains the Continuous bag of words and skip-gram models, which are deep neural nets.

The areas in robotics and information processing where sequential prediction problem arises are

☛ a) Imitation Learning

☛ b) Structured prediction

☛ c) Model based reinforcement learning

D) None of the above

None if these expressions would be able to identify the dates in this text object.

B) Dependency Parsing and Constituency Parsing

Dependency and constituent parsing extract these relations from the text

The general principle of an ensemble method is to combine the predictions of several models built with a given learning algorithm in order to improve robustness over a single model. Bagging is a method in ensemble for improving unstable estimation or classification schemes. While boosting method are used sequentially to reduce the bias of the combined model. Boosting and Bagging both can reduce errors by reducing the variance term.

A) 0

LDA is unsupervised learning model, LDA is latent Dirichlet allocation, not Linear discriminant analysis. Selection of the number of topics is directly proportional to the size of the data, while number of topic terms is not directly proportional to the size of the data. Hence none of the statements are correct.

Incremental learning method is the ability of an algorithm to learn from new data that may be available after classifier has already been generated from already available dataset.

Bayesian logic program consists of two components. The first component is a logical one ; it consists of a set of Bayesian Clauses, which captures the qualitative structure of the domain. The second component is a quantitative one, it encodes the quantitative information about the domain.

Machine learning in where mathematical foundations is independent of any particular classifier or learning algorithm is referred as algorithm independent machine learning?

E) All of these

All of the techniques can be used for the purpose of engineering features in a model.

Designing and developing algorithms according to the behaviours based on empirical data are known as Machine Learning. While artificial intelligence in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc.

D) 1, 2, 3

All of the techniques can be used to reduce the dimensions of the data.

PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning algorithms and their statistical efficiency.

PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction.

The process of selecting models among different mathematical models, which are used to describe the same data set is known as Model Selection. Model selection is applied to the fields of statistics, machine learning and data mining.

D) All of the above

word2vec model can be used for measuring document similarity based on context. Bag Of Words and document term matrix can be used for measuring similarity based on terms.

D) Keyword Hashing

Except Keyword Hashing all other are the techniques used in flexible string matching

The different types of techniques in Machine Learning are

☛ a) Supervised Learning

☛ b) Unsupervised Learning

☛ c) Semi-supervised Learning

☛ d) Reinforcement Learning

☛ e) Transduction

☛ f) Learning to Learn