What Is Lexical Analysis in NLP?

22-Nov-2024

Whenever we talk about computers, one of the most interesting as well as difficult tasks would be understanding and processing human languages. This is where Natural Language Processing (NLP) becomes significant. NLP is a domain of computer science created with the aim of making machines understand, interpret and generate human language. One such step in this regard is known as lexical analysis.

But what is lexical analysis in particular, and why is it crucial for natural language processing? In this article, our attempt would be to explain how lexical analysis enables machines to understand words and sentences.

What is lexical analysis?

The process of Lexical analysis involves breaking down a piece of text into smaller yet manageable fragments known as tokens. Now, these tokens can be described as the building blocks of language that a machine can work with. If you have a sentence like "I love reading books", lexical analysis would break it into singular tokens such as:

•    I

•    love

•    reading

•    books

Each of the tokens presents a meaningful unit in the sentence. This unit can be words, numbers, punctuation marks, or other symbols, having specific meaning in the language. Simply, lexical analysis is similar to taking a sentence and chopping it up into its individual fragments or parts so that the computer can comprehend and analyze each piece more easily.

Why is lexical analysis important?

If you want to understand how machines comprehend and process language, consider the example about how you, as a human, read a sentence. A person gets the meaning of the word, almost instantly by looking at the word, due to his or her command of the language. It is, however, very different for a computer. A computer system does not inherently comprehend the semantics behind lexicons and strings of words. They require assistance to decompose the content and organize it in a comprehensible format.

Lexical analysis helps in this by:

1. Breaking down text: It would be difficult for the machine to analyze or recognize the text until it breaks the text into smaller parts.

2. Identifying meaningful units: The computer can interpret the sentence by identifying words, numbers, and punctuations.

3. Preparing text for further processing: Post lexical analysis, the text is prepared for other further NLP operations such as syntactic analysis (the arrangement of words) and semantic analysis (the understanding of meaning).

Lexical analysis is the first step toward making sense of human language, and without this process, machines would struggle to analyze even the simplest of sentences.

Underlying mechanisms of lexical analysis

Let’s imagine you're reading a book. As you read, you recognize individual words and punctuation. A computer does something similar, but with a few extra steps. Here's how lexical analysis works in a simplified way:

1. Text input: The process initializes with raw text input. This could be a sentence, a paragraph, or even an entire document.

2. Tokenization: The foremost thing that machine does is split the text into tokens. This implies separating words, punctuation, and numbers. For instance, the sentence "I love playing golf." would be tokenized into the following tokens:

• I
• love
• playing
• golf
• .

3. Normalization: Once the text is segmented into its individual components or tokens, the subsequent procedure is to realize the text in its normalized form. This may include standardization of the case to lower or upper, removing unwanted curve quotes or punctuations, or handling things such as replacing ‘don’t’ with ‘do not’. This stage guarantees uniformity and simplifies analysis.

4. Tools for lexical analysis: Lexical analysis is achieved through the use of numerous tools and methods. These tools include regular expressions, lexers, and dictionaries.

5. Token classification: The different categories into which the tokens fall are determined by the functions they perform in a sentence. For instance, a token may be recognized as a noun, verb, adjective, punctuation mark.

6. Challenge of ambiguity: The process of lexical analysis faces a challenge in dealing with ambiguity. Consider the example of word "bank", which could mean a financial institution or the side of a river. Lexical analysis needs to perceive the correct meaning based on the context.

Types of tokens in lexical analysis

In any text, you’ll encounter different types of tokens. These tokens can be grouped into several categories. Here are some common types:

• Words: These are the most common and obvious type of token. For example, in the sentence "Cats are friendly" , the tokens would be - "Cats," "are," and "friendly".

• Punctuation: Punctuation marks are also considered as tokens. They can help in structuring a sentence and provide meaning to how it is read.

• Special characters: There might be special characters as well apart from words and punctuations. Depending on the context, special characters could be like hashtags (#), at signs (@), or even emoticons.

• Numbers: Any numerical values, like 1, 20, or 3.14, are also considered as tokens. In NLP, numbers are often handled differently than words.

Examples of lexical analysis in NLP

Let's have a glance at a few examples depicting the workings of lexical analysis:

1. Search engines: When the user types a query into Google search engine, the first activity that is performed is lexical analysis. The search engine needs to understand your request better, and for that, it divides the query into several tokens (words or phrases). Suppose you enter the query "best pizza near me", the engine will then perform the operation of tokenizing this query and provides the output as "best", "pizza", "near", and "me".

2. Chatbots: Lexical analysis is used in a chatbot conversation to understand the user's message. For example, if a user writes a sentence: “How is it going to be the weather today?”, a chatbot will analyze it on the level of tokens. It will try to recognize what you are asking about the weather.

3. Sentiment analysis: The aim of sentiment analysis is to evaluate the degree of sentiment, neutrality, positivity or negativity of a given text. Lexical analysis plays a crucial part in performing sentiment analysis. It first breaks the text into tokens, after which these tokens are analyzed to try and understand the sentiments carried by them.

Difficulties in lexical analysis

Some of the main difficulties in lexical analysis include:

• Problem of ambiguity: There are a number of terms that have different connotations based on the surrounding words. For example, the term “bark” can mean the outer covering of a tree or the sound a dog makes. Lexical analysis needs to mitigate this ambiguity. This can be done by looking at the nearby words to understand the correct meaning.

• Compound words: Some languages use long compound words, like German language. Lexical analysis must figure out the way to break these words into meaningful tokens. For instance, German word "Fernsehen" (television) might be split into "fern" (far) and "sehen" (see), but the meaning comes together when combined.

• Use of informal language: At present, many individuals prefer an informal language while communicating, incorporating a lot of slang and abbreviating several words. Thus, when doing any lexical analysis, it is important to identify such non-standard usages of language and relate them meaningfully to the context.

Conclusion

The process of lexical analysis is a vital concept in NLP, as it enables machines to read and understand the language as humans do. This involves breaking the text into smaller and manageable tokens so that it is easy for a computer to comprehend a word, a group of words and a sentence. This is often seen as the simplest step but is a crucial foundation which enables the machines to interact with a human being. With the development of smarter machines, the emphasis on lexical analysis will only increase.

Lexical Analysis in NLP Why is lexical analysis important? Difficulties in lexical analysis