- Natural Language Processing it is a branch of data science that focuses on training computers to process and interpret conversations in text format in a way humans do by listening.
- NLP applications are difficult and challenging during development as computers require humans to interact with them using programming languages like Java, Python, etc., which are structured and unambiguous.
- Application of natural language processing, data science, ML, and AI has changed the way we interact with computers, and it will continue to do so in the future.
Natural Language Processing (NLP) is a prominent branch of artificial intelligence (AI) within data science, dedicated to extracting insights from textual data. This has led to a surge in demand for NLP professionals, as every conversation and expression harbors valuable information crucial for decision-making.
However, extracting insights from text data presents a formidable challenge, given the myriad languages, expressions, and tones humans employ. The data generated from our daily interactions is inherently unstructured. Yet, advancements in data science and NLP techniques have enabled machines to engage in meaningful conversations with humans. In this article, we’ll explore and delve into the ten most widely used NLP techniques in data science.
Also read: The difference between Conversational AI and GenAI
1. Tokenisation in NLP
Tokenisation, a fundamental NLP technique, involves segmenting text into sentences and words, essentially dividing it into tokens. This process eliminates certain characters like punctuation and hyphens to render the text more analytically manageable.
Consider this example: when tokenising, the text is typically divided by blank spaces. However, issues may arise, particularly with punctuation. For instance, in the case of abbreviations like “Mr.,” the period should ideally be retained as part of the same token, but tokenisation may erroneously split it into two words. This challenge becomes more pronounced in domains with complex biomedical text containing numerous hyphens, parentheses, and punctuations, leading to potential complications during tokenisation.
Also read: Exploring the best conversational AI platforms
2. Stemming and lemmatisation
The primary goal of stemming in NLP is to reduce words to their root form, aiming to group together variations of words with the same meaning. Stemming achieves this by removing affixes from words, streamlining processing for efficiency.
In contrast, lemmatisation involves converting words to their dictionary form, known as the lemma. For instance, “hates” and “hating” are variations of the word “hate,” with “hate” being the lemma for both. The objective of lemmatisation is similar to stemming—grouping different forms of words together—but employs a distinct approach.
3. Stop words removal
TF, or Term Frequency, quantifies the frequency of a word within a specific document. It is computed by tallying the total occurrences of the word and dividing it by the document’s total length, expressed as TF = Total occurrences / Total length of the document.
On the other hand, IDF, or Inverse Document Frequency, assigns a weight to each word based on its significance. This is determined by taking the logarithm of the total number of documents in the dataset divided by the number of documents containing that particular word.
TF-IDF, the product of TF and IDF, provides a measure of a word’s importance. Words with higher importance are assigned greater weights through this statistical calculation. This technique is widely employed by search engines to score and rank the relevance of documents in response to input keywords.
4. Term frequency-inverse document frequency (TF-IDF)
TF or Term frequency measures the frequency of a word in a given document. This is calculated by counting the total number of occurrences of the word and dividing it by the total length of the document i.e – TF=Total occurrences/Total length of the document.
IDF or Inverse Document Frequency assigns a weight to any string according to its importance. It calculates it by taking the log of the total number of documents in the dataset present at that time divided by the number of documents containing that particular word. TF-IDF is the importance of any word by multiplying the TF and IDF terms i.e TF*IDF.
Thus, by this method, the words having more importance are assigned higher weights by using these statistics. TF-IDF technique is mostly used by search engines for scoring and ranking the relevance of any document according to the given input keywords.
5. Keyword extraction in NLP
Keyword extraction is a text analysis method that automatically identifies the most prominent words and phrases in a given text. This technique aids in summarising content and identifying key topics discussed.
It operates across various text sources, including documents, social media posts, online forums, and news reports. By employing keyword extraction, businesses can efficiently discern prevalent customer mentions on the internet, saving significant time compared to traditional manual processing methods.
Given that over 80% of daily data is unstructured, automated keyword extraction is indispensable for businesses seeking to analyse customer data efficiently.
