The industrial application of natural language processing (NLP) is on the rise, with increased expenditure to enhance its usage. Three-quarters of organizations using NLP expect to increase their investments in it in the upcoming months. But where can we witness NLP in daily life? Search using Google, Bing or ChatGPT, and the relevant query-based results you get are obtained using NLP.
The article will introduce you to natural language processing, the sub-category of artificial intelligence, linguistics, computer science, and machine learning aimed at analyzing human data.
Essential Components of NLP Associated with Text Data Analysis
Natural Language Understanding (NLU), is responsible for understanding and analysis of human language. It primarily involves two tasks, intent identification of human language, such as entities, sentiments and patterns. Secondly, it transforms human language into a structured and meaningful format for computer processing.
Natural Language Generation (NLG), translates computer-generated data into natural language representation. It involves components like sentence and text planning and text realization.
Tasks by Natural Language Processing
The response of bots or software using NLP is based on the training datasets containing huge amounts of information. The transfer of information can be through written or verbal communication in any language. Further, the data also comprises phrases, topics, tones and sentiments that need to be accurately extracted from the information for better response to form a dataset.
The arrangement of words (syntax) and the interpretation or meaning of sentences (semantics) or context of sentences (pragmatics) needs to be accurately recognized for the results. NLP performs distinct tasks to ensure the right interpretation of syntax and semantics, which are enlisted as follows:
Tokenization
The input prompts are first processed by breaking down the structure into simpler understandable levels. These smaller units are tokens split into words, sentences, punctuations, characters and punctuation depending on the type of input. The word tokens are separated with commas or blank spaces, while sentence tokens are separated by stops. The phrases or collocations are kept together during the development of tokens.
Example:
Prompt: “NLP is widely used. Enlist the involved companies.”
Sentence tokenization:
Automation is widely used.
Enlist the involved companies.
Word tokenization:
“NLP” “is” “widely” “used”. “Enlist” “the” “involved” “companies”.
Part of Speech Tagging
Words have different natures, nouns, pronouns, adjectives, numbers, people, verbs and similar others. NLP recognizes each type of word and tags it accordingly to understand the relationship and meaning of sentences.
Example:
NLP: Noun, is: verb, widely: an adverb, used: verb, Enlist: verb, the: determiner, involved: Verb and companies: Noun
Parsing
It is done to understand the grammar of a sentence or the syntax (arrangement of words). The task is done via three types, constituency, chunking or shallow and dependency parsing.
Constituency parsing checks the sentence grammar through constituents or individual components and analyzes it in a hierarchical manner. It forms a parse or syntactic tree.
Chunking parsing is concerned with meaning extraction from the test by identifying the chunks as a Verb phrase (VP), Adverb Phrase (AP), Adjective Phrase (ADJP) and Prepositional Phrase (PP).
Dependency parsing analyzes grammatical relationships between the words of sentences. It focuses on the main word and the dependencies between the words. It produces a directed graph referred to as a dependency tree.
Example:
Prompt: “Rhea ate a cake.”
The dependency tree on parsing will be as follows:
Stemming and Lemmatization
Words are modified to suit the grammar. NLP identifies and transforms the word to root form (lemma) through stemming. For instance, the lemma for ‘feet’ is ‘foot’, for ‘is, been, were’ is ‘be’, for ‘consultant’ or ‘consulting’ is ‘consult’. Remember that lemmatization is a dictionary-based approach considering the context while stemming does not consider the context.
Stopword removal
Sentences often contain words like ‘the’, ‘of’, ‘is’ and ‘with’. These are of very little importance, and NLP filters out high-frequency words. Users can customize the lists based on their needs.
Vectorization
Computers understand numbers rather than words. Hence, every word is given a numerical vector. It involves different approaches, where Bag or Words and Term Frequency – Inverse Document Frequency (TFIDF) is primitive while word, document or Transformer embeddings are complex and contextual. It is represented with the matrix.
Named Entity Recognition (NER)
NER is crucial for semantic analysis and text extraction. The entity in NER is name, address, location, email or others. Another similar task is relationship extraction and named entity disambiguation. NLP finds the relationship between two nouns in the first one and identifies the context of the word. For instance, it recognizes if the apple in the prompt is a fruit or brand.
Word Sense Disambiguation
Words have several meanings or are polysemic in nature. For instance, the word plant can refer to botanical plants, industrial plants, vegetation and specific types of living organisms. Distinguishing the same by NLP is based on a knowledge-based or supervised approach. NLP either looks at the dictionary definitions of such ambiguous terms or refers to the learned data for understanding.
Text classification
The prompts may or may not be well structured. Text classification rectifies unstructured text by classifying and organizing it through predefined categories or tags. It involves sentiment analysis, language and intent detection and topic modeling.
Example:
Prompt: “NLP is widely used. Enlist the involved companies.”
This would likely be classified as a “Request” since it is asking for a list of involved companies.
How is Text Data Analysis Made Easy by Natural Language Processing?
The workflow of natural processing involves the following sequence of steps:
Preprocessing involves cleaning and preparation of data through activities like removing the special characters, handling cases and formatting issues.
Tokenization, stopword removal, stemming/lemmatization and vectorization are among the previously discussed NLP tasks.
Model training involves building an NLP algorithm through two main approaches:
- Rule-based approach
- Machine learning algorithm
The rule-based approach was popular earlier, where grammatical rules were manually created by experts from different fields. The shift to natural language processing machine learning is now based on statistical methods, thus automating the learning process.
Model evaluation is a way to test how well the trained model is working. It helps ensure that the model isn’t just memorizing what it witnessed during training and actually understands the language patterns.
The inference and prediction vary based on trained models. Further, the obtained result is refined through post-processing to correct and improve the results. It can involve tasks like correcting grammatical errors, removing low-confidence predictions and others. Additionally, the final outputs also vary with different tasks. For instance, it can translate, summarize and classify labels and sentiment scores.
Uplevel Your Knowledge ML Today
Natural language processing is a widely used significant part of Artificial Intelligence and ML. It helps with the interpretation of human language, bridging the gap between the human and computer world. Natural language processing and machine learning combine together to provide algorithms for the models and provide the desired output. Numerous advancements have always taken place while many are on the way.
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FAQs about Natural Language Processing
Q1. What libraries are used in Machine learning natural language processing?
TensorFlow is amongst the most popular ML libraries that can also be used for Natural Language Processing tasks. It helps with tasks such as text classification, sentiment analysis and machine translation. Other libraries include Natural Language Toolkit (NLTK), Apache OpenNLP, and more.
Q2. What are some applications of natural language processing?
Some applications of NLP include sentiment analysis of text, chatbots and virtual assistants, translation between different languages, speech recognition, text summarization, information retrieval from vast amounts of text data based on user queries, clinical documentation, disease detection, analysis of financial reports, and more.
Q3. How to learn natural language processing?
To learn NLP, you must have a basic knowledge of programming languages like Python or Keras. You should also understand the basics of cleaning text data and manual tokenization. Enrolling in an online course assists you in speeding up the NLP learning process.
Q4. Is NLP high paying?
Yes, an NLP job position can be rewarding. The entry-level positions start at $126,050 per year, while the average NLP engineer salary in the USA is $160,000 per year or $76.92 per hour.
Q5. What are some common applications of NLP?
NLP is used in chatbots, virtual assistants, sentiment analysis, machine translation, and text summarization across various industries.
