Natural Language Processing (NLP): 7 Key Techniques

Natural Language Processing (NLP) is a subfield in Deep Learning that makes machines or computers learn, interpret, manipulate and comprehend the natural human language. Natural human language comes under the unstructured data category, such as text and voice. Generally, computers can understand the structured form of data, such as tables and spreadsheets in the database, since the human language, like text and voice, comes under the category of unstructured data, and since computers can’t understand the unstructured textual or voice data, NLP comes into action.

In this article, we will explore about 7 Natural Language Processing Techniques that form the backbone of numerous applications across various domains.

Natural Language Processing (NLP) techniques are methods and algorithms used to process, analyze and understand human language and data. Thes techniques enable systems to interact with interpret and generate natural language text. Here are some common NLP Techniques that are mentioned below:-

Named Entity Recognition or NER is used to identify entities and classify them into predefined categories, where entities include things like person names, organizations, locations, and named items in the text. This technique is very important for information extraction and by using this you get sense of large volumes of unstrucutred data by identifying entities and categorizing them into predefined cateogories.

For a given piece of data like text or voice, Sentiment Analysis determines the sentiment or emotion expressed in the data, such as positive, negative, or neutral. This technique is widely used in social media monitoring, customer feedback analysis, and market research. Many big tech companies use this technique and these results provide customer insights and strategic outcomes.

Text summarization basically converts a larger data like a text documents to the most concise shorter version while retaining the important essential information. This technique helps us to easily and quickly grasp the required main points of larger texts, resulting in efficient information retrieval and management of the large content. Text Summarizatin is also called as Automated Summarization that basically condenses the text data while preserving its details.

Topic Modeling comes under unsupervised Natural Language Processing (NLP) technique that basically makes use Artificial Intelligence (AI) programs to tag and classify text clusters that have topics in common. The purpose. of Topic Modeling is to represent each document of the dataset as the combination of different topics, which will makes us gain better insights into the main themes present in the text corpus.

Text Classification is the classification of large unstructured textual data into the assigned category or label for each document. Topic Modeling, Sentiment Analysis, Keywords Extraction are all subsets of text classification. This technique generally involves collecting information from the customer reviews and customer service slogs.

For a given piece of text, Keyword Extraction technique identifies and retrieves words or phrases from the text. The main objective of this technique involves identifying the meaningful terms from the text, which represents the important ideas or information present in the document. Keyword Extraction is distinct from text classification and topic modeling, because text classification assigns predefined cateogires to documents, and topic modeling uncovered latent topics withing a text corpus, but keyword extraction focuses on extracting specific words or phrases that are highly informative and represents the crucial information of the document.

More technical than our previously discussed techniques, lemmatization and stemming are basically used to reduce the words to their base forms or root forms, converting them into more manageable data for text processing or text analysis.

Lemmatization is an advanced NLP technique that uses a lexicon or vocabulary to convert words into their base or dictionary forms called lemms. Now the lemmatized word is a valid words that represents base meaning of the original word. Lemmatization considers the part of speech (POS) of the words and ensures that the output is a proper words in the language.

Stemming is a faster and simpler NLP technique, that basically truncates words into their stems, the choice between lemmatization and stemming dependson the specific requirements of the NLP project or task, the language being used, and the desired level of accuracy required.

Despite its advancements, the domain of NLP have several challenges that includes:

• Context Understanding: A lot of times, understanding and capturing the context of a particular conversation or text is very difficult, but this is very important for accurate interpretation of the models.
• Ambiguity: There are many human languages in the world and they are often ambiguous, with different languages having words that have multiple various meaning based on the context.
• Data Privacy: It is very challenging to ensure the privacy of the user data, in the case of processing large volumes of personal text data of theirs.
• Language Diversity: This is considered to be most challenging task, that is to develop the machine learning models that work accurately for various languages and dialects.

The future of NLP in this era of Artificial Intellligence (AI) is very promising, with several trends shaping its evolution, that includes:

• Multimodal NLP: Integrating text, speech, and visual data for a more comprehensive understanding of context and meaning.
• Explainable AI: Developing models that provide clear explanations for their decisions and outputs.
• Low-Resource Language Processing: Improving NLP capabilities for languages with limited available data.
• Personalization: Tailoring NLP applications to individual user preferences and behaviors for more personalized experiences.

Natural Language Processing is a rapidly advancing field that has revolutionized how we interact with technology. By understanding and implementing key NLP techniques like Stemming and Lemmatization, Named Entity Recognition (NER), Text Summarization, Sentiment Snalysis, Text Classification, Keyword Extraction, Topic Modeling, we can unlock the full potential of human language data. As NLP continues to evolve, it will play an increasingly vital role in various industries, driving innovation and improving our interactions with machines.