Natural Languages Processing Assignment 2022

Running head: NATURAL LANGUAGE PROCESSING
NATURAL LANGUAGE PROCESSING
Name of student
Name of university
Author’s note:

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NATURAL LANGUAGE PROCESSING
1. Introduction and Background
Natural Language Processing, referred to as NLP is an important subfield of artificial
intelligence or AI. It provides context for human computer interaction through natural
language that is considered for human conversation. Interaction between human and
computers are limited to application of programming language. However, one of the main
objectives of artificial intelligence is to make machines more intelligence which are capable
of processing human queries in most natural way possible and for this, identification and
processing of natural language is of significant importance (Goldberg, 2017). Natural
language processing is therefore becoming an interesting topics among researchers for its
capability in making machines more intelligent and make conversation with humans in
natural language. It will not only make conversation more effective but it will enhance value
of conversation as well. NLP is applied for reading and decoding human language so that it
make sense to machines and provide output that is meaningful and valuable as well.
2. Outline of the methods, techniques that support this technology
Natural language is having a significant potential in transforming applications of artificial
intelligence. However, processing natural language by machines is not an easy thing to
accomplish and therefore, for this various techniques are being created and these techniques
are based on applications of artificial intelligence or AI (Young et al., 2018). In this report
some important AI based techniques for natural language processing are analysed.
Word embeddings:
Distributional vectors also known as word embeddings is an important technique for natural
language processing. This method is based on shallow neural networks, an important aspect
of artificial intelligence (Yin et al., 2017). This technique follows distributional hypothesis
which say that words that appear within same context are having similar meaning. Word

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embeddings while applied on a task require some modifications. In order to apply this
technique, the task is first pre-trained and its object is to make prediction of a word according
to its context. To implement this, it requires application of shallow neural networks (Sun, Luo
& Chen, 2017). In shallow neural networks, there is only one hidden layer compared to deep
neural network that where several hidden layers are present. Therefore, this layer is relatively
easy to apply but it is not applicable for process where sophisticated natural language
processing is required. However, word vectors are capable of embedding syntactical and also
semantic information which helps in some important NLP related tasks for example analysis
of sentiment in sentence and composing sentences as well.
A neural language model for word embedding is provided in this context.
Convolutional Neural Network
Convolutional Neural Network or CNN is a technique that is based on neural networking
approach. It provides a feature function which constitute words or n-grams for extracting
higher level features from a language (Goldberg, 2017). This technique is therefore
considered for applications like analysing sentiment in a sentence, machine translation and

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question answer which requires advanced language processing. In this technique, words are
transformed into vector and it is then represented with a look-up table. It provides an
approach for word embedding where neural network is trained and it learns weights. This
process is described through a model provided in this context.