Understanding Semantic Analysis – NLP

Introduction to Semantic Analysis

Semantic Analysis is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language. Understanding Natural Language might seem a straightforward process to us as humans. However, due to the vast complexity and subjectivity involved in human language, interpreting it is quite a complicated task for machines. Semantic Analysis of Natural Language captures the meaning of the given text while taking into account context, logical structuring of sentences and grammar roles.

Parts of Semantic Analysis

Semantic Analysis of Natural Language can be classified into two broad parts:

1. Lexical Semantic Analysis: Lexical Semantic Analysis involves understanding the meaning of each word of the text individually. It basically refers to fetching the dictionary meaning that a word in the text is deputed to carry.

2. Compositional Semantics Analysis: Although knowing the meaning of each word of the text is essential, it is not sufficient to completely understand the meaning of the text.

For example, consider the following two sentences:

• Sentence 1: Students love GeeksforGeeks.
• Sentence 2: GeeksforGeeks loves Students.

Although both these sentences 1 and 2 use the same set of root words {student, love, geeksforgeeks}, they convey entirely different meanings.

Hence, under Compositional Semantics Analysis, we try to understand how combinations of individual words form the meaning of the text.

Tasks involved in Semantic Analysis

In order to understand the meaning of a sentence, the following are the major processes involved in Semantic Analysis:

1. Word Sense Disambiguation
2. Relationship Extraction

Word Sense Disambiguation:

In Natural Language, the meaning of a word may vary as per its usage in sentences and the context of the text. Word Sense Disambiguation involves interpreting the meaning of a word based upon the context of its occurrence in a text.

For example, the word ‘Bark’ may mean ‘the sound made by a dog’ or ‘the outermost layer of a tree.’

Likewise, the word ‘rock’ may mean ‘a stone‘ or ‘a genre of music‘ – hence, the accurate meaning of the word is highly dependent upon its context and usage in the text.

Thus, the ability of a machine to overcome the ambiguity involved in identifying the meaning of a word based on its usage and context is called Word Sense Disambiguation.

Relationship Extraction:

Another important task involved in Semantic Analysis is Relationship Extracting. It involves firstly identifying various entities present in the sentence and then extracting the relationships between those entities.

For example, consider the following sentence: 

Semantic Analysis is a topic of NLP which is explained on the GeeksforGeeks blog. The entities involved in this text, along with their relationships, are shown below.

Entities

Relationships

Elements of Semantic Analysis

Some of the critical elements of Semantic Analysis that must be scrutinized and taken into account while processing Natural Language are:

• Hyponymy: Hyponymys refers to a term that is an instance of a generic term. They can be understood by taking class-object as an analogy. For example: ‘Color‘ is a hypernymy while ‘grey‘, ‘blue‘, ‘red‘, etc, are its hyponyms.
• Homonymy: Homonymy refers to two or more lexical terms with the same spellings but completely distinct in meaning. For example: ‘Rose‘ might mean ‘the past form of rise‘ or ‘a flower‘, – same spelling but different meanings; hence, ‘rose‘ is a homonymy.
• Synonymy: When two or more lexical terms that might be spelt distinctly have the same or similar meaning, they are called Synonymy. For example: (Job, Occupation), (Large, Big), (Stop, Halt).
• Antonymy: Antonymy refers to a pair of lexical terms that have contrasting meanings – they are symmetric to a semantic axis. For example: (Day, Night), (Hot, Cold), (Large, Small).
• Polysemy: Polysemy refers to lexical terms that have the same spelling but multiple closely related meanings. It differs from homonymy because the meanings of the terms need not be closely related in the case of homonymy. For example: ‘man‘ may mean ‘the human species‘ or ‘a male human‘ or ‘an adult male human‘ – since all these different meanings bear a close association, the lexical term ‘man‘ is a polysemy.
• Meronomy: Meronomy refers to a relationship wherein one lexical term is a  constituent of some larger entity. For example: ‘Wheel‘ is a meronym of ‘Automobile‘

Meaning Representation

While, as humans, it is pretty simple for us to understand the meaning of textual information, it is not so in the case of machines. Thus, machines tend to represent the text in specific formats in order to interpret its meaning. This formal structure that is used to understand the meaning of a text is called meaning representation.

Basic Units of Semantic System:

In order to accomplish Meaning Representation in Semantic Analysis, it is vital to understand the building units of such representations. The basic units of semantic systems are explained below:

1. Entity: An entity refers to a particular unit or individual in specific such as a person or a location.  For example GeeksforGeeks, Delhi, etc.
2. Concept: A Concept may be understood as a generalization of entities. It refers to a broad class of individual units. For example Learning Portals, City, Students.
3. Relations: Relations help establish relationships between various entities and concepts. For example: ‘GeeksforGeeks is a Learning Portal’, ‘Delhi is a City.’, etc.
4. Predicate: Predicates represent the verb structures of the sentences.

In Meaning Representation, we employ these basic units to represent textual information.

Approaches to Meaning Representations:

Now that we are familiar with the basic understanding of Meaning Representations, here are some of the most popular approaches to meaning representation:

1. First-order predicate logic (FOPL)
2. Semantic Nets
3. Frames
4. Conceptual dependency (CD)
5. Rule-based architecture
6. Case Grammar
7. Conceptual Graphs

Semantic Analysis Techniques

Based upon the end goal one is trying to accomplish, Semantic Analysis can be used in various ways. Two of the most common Semantic Analysis techniques are:

Text Classification

In-Text Classification, our aim is to label the text according to the insights we intend to gain from the textual data.

For example:

• In Sentiment Analysis, we try to label the text with the prominent emotion they convey. It is highly beneficial when analyzing customer reviews for improvement.
• In Topic Classification, we try to categories our text into some predefined categories. For example: Identifying whether a research paper is of Physics, Chemistry or Maths
• In Intent Classification, we try to determine the intent behind a text message. For example: Identifying whether an e-mail received at customer care service is a query, complaint or request.

Text Extraction

In-Text Extraction, we aim at obtaining specific information from our text.

For Example, 

• In Keyword Extraction, we try to obtain the essential words that define the entire document.
• In Entity Extraction, we try to obtain all the entities involved in a document.

Significance of Semantics Analysis

Semantics Analysis is a crucial part of Natural Language Processing (NLP). In the ever-expanding era of textual information, it is important for organizations to draw insights from such data to fuel businesses. Semantic Analysis helps machines interpret the meaning of texts and extract useful information, thus providing invaluable data while reducing manual efforts.

Besides, Semantics Analysis is also widely employed to facilitate the processes of automated answering systems such as chatbots – that answer user queries without any human interventions.

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• Machine Learning
• NLP
• Natural-language-processing
• python

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