NLP - Word Sense Disambiguation

 

We understand that words have different meanings based on the context of its usage in the sentence. If we talk about human languages, then they are ambiguous too because many words can be interpreted in multiple ways depending upon the context of their occurrence.

Word sense disambiguation, in natural language processing (NLP), may be defined as the ability to determine which meaning of word is activated by the use of word in a particular context. Lexical ambiguity, syntactic or semantic, is one of the very first problem that any NLP system faces. Part-of-speech (POS) taggers with high level of accuracy can solve Word’s syntactic ambiguity. On the other hand, the problem of resolving semantic ambiguity is called WSD (word sense disambiguation). Resolving semantic ambiguity is harder than resolving syntactic ambiguity.

Natural Language Discourse Processing

 

The most difficult problem of AI is to process the natural language by computers or in other words natural language processing is the most difficult problem of artificial intelligence. If we talk about the major problems in NLP, then one of the major problems in NLP is discourse processing − building theories and models of how utterances stick together to form coherent discourse. Actually, the language always consists of collocated, structured and coherent groups of sentences rather than isolated and unrelated sentences like movies. These coherent groups of sentences are referred to as discourse.

Concept of Coherence

Part of Speech (PoS) Tagging

Tagging is a kind of classification that may be defined as the automatic assignment of description to the tokens. Here the descriptor is called tag, which may represent one of the part-of-speech, semantic information and so on.

Now, if we talk about Part-of-Speech (PoS) tagging, then it may be defined as the process of assigning one of the parts of speech to the given word. It is generally called POS tagging. In simple words, we can say that POS tagging is a task of labelling each word in a sentence with its appropriate part of speech. We already know that parts of speech include nouns, verb, adverbs, adjectives, pronouns, conjunction and their sub-categories.

Natural Language Processing - Inception

 

In this chapter, we will discuss the natural language inception in Natural Language Processing. To begin with, let us first understand what is Natural Language Grammar.

Natural Language Grammar

NLP - Information Retrieval

 

Information retrieval (IR) may be defined as a software program that deals with the organization, storage, retrieval and evaluation of information from document repositories particularly textual information. The system assists users in finding the information they require but it does not explicitly return the answers of the questions. It informs the existence and location of documents that might consist of the required information. The documents that satisfy user’s requirement are called relevant documents. A perfect IR system will retrieve only relevant documents.

Applications of NLP

 

Natural Language Processing (NLP) is an emerging technology that derives various forms of AI that we see in the present times and its use for creating a seamless as well as interactive interface between humans and machines will continue to be a top priority for today’s and tomorrow’s increasingly cognitive applications. Here, we are going to discuss about some of the very useful applications of NLP.

Natural Language Processing - Python

 

In this chapter, we will learn about language processing using Python.

The following features make Python different from other languages −

• Python is interpreted − We do not need to compile our Python program before executing it because the interpreter processes Python at runtime.

• Interactive − We can directly interact with the interpreter to write our Python programs.

• Object-oriented − Python is object-oriented in nature and it makes this language easier to write programs because with the help of this technique of programming it encapsulates code within objects.

• Beginner can easily learn − Python is also called beginner’s language because it is very easy to understand, and it supports the development of a wide range of applications.

   

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