Artificial intelligence (AI) envisions creating machines that imitate human intelligence and behave like us. According to the erudite scholar Yuval Noah Harari, language is what sets humans apart from other animals. Many consider it to be the most significant achievement of homo sapiens, one which has enabled us to cooperate in large numbers with each other.
Thus, it should not come as a surprise to anyone that humans are actively trying to integrate languages into machines and software through the field of artificial intelligence. They are doing this through a process called Natural Language Processing NLP.
What is NLP?
Natural language processing hereafter referred to as NLP, is the AI-powered process of rendering human language input comprehensible and decipherable to software and machines.
NLP essentially consists of natural language understanding (human to machine), also known as natural language interpretation, and natural language generation (machine to human.)
Natural Language Understanding (NLU) – Refers to the techniques that aim to deal with the syntactical structure of a language and derive semantic meaning from it. Examples include Named Entity Recognition, Speech Recognition, and Text Classification.
Natural Language Generation (NLG) – It takes the results of NLU a step ahead with language generation. Examples include Text Generation, Question Answering, and Speech Generation.
Let’s look at the leading NLP techniques now.
Top 10 NLP Techniques
Tokenization is one of the most essential and basic NLP techniques. It is a vital step for processing text for an NLP application whereby you take a long-running text string and break it down into smaller units. Each unit is called a token, representing a word, symbol, number, etc.
These tokens aid in understanding the context when developing NLP models. As such, they are the building blocks of a model. Many tokenizers use a blank space as a separator to create tokens. Here are some of the tokenization techniques employed in NLP, depending upon your goal:
- White Space Tokenization
- Rule-based Tokenization
- Spacy Tokenizer
- Dictionary-based Tokenization
- Subword Tokenization
- Penn Tree Tokenization
2. Stemming and Lemmatization
Stemming or lemmatization is the next most important NLP technique in the preprocessing phase. It refers to reducing a word to its word stem that attaches to a prefix or suffix. Lemmatization refers to the text normalization technique whereby any kind of word is switched to its base root mode.
Search engines and chatbots use these two techniques to understand the meaning of a word. Both techniques aim to generate the root word of any word. While stemming focuses on removing the prefix or suffix of a word, lemmatization is more sophisticated in that it generates the root word through morphological analysis.
3. Stop Words Removal
Stop word removal is the next step in the preprocessing phase after stemming and lemmatization. Many words in a language serve as fillers; they don’t really have a meaning of their own—for example, conjunctions like since, and, because, etc. Prepositions like in, at, on, above, etc., are also fillers.
Such words don’t serve any significant purpose in an NLP model. However, it is not mandatory to stop word removal for every model. The decision depends on the kind of task. For example, when implementing text classification, stop word removal is a helpful technique. But machine translation and text summarization do not require stopping word removal.
You can use various libraries like SpaCy, NLTK, and Gensim for stop words removal.
TF-IDF is actually a statistical method used to show the importance of a given word for a document in a compendium of documents. To calculate the TF-IDF statistical measure, you multiply two distinct values (term frequency and inverse document frequency).
Term Frequency (TF)
It is used to calculate the frequency of a word’s occurrence in a document. Use the following formula to calculate it:
TF (t, d) = count of t in d/ number of words in d
Words like “is,” “the,” and “will” usually have the highest frequency term frequency.
Inverse Document Frequency (IDF)
Before explaining IDF, let’s understand Document Frequency first. Document Frequency calculates the presence of a word in a collection of documents.
IDF is the opposite of Document Frequency. It calculates the importance of a term in a corpus of documents. Words that are specific to a document will have high IDF.
The idea behind TF-IDF is to find prime words in a document by looking for words having a high frequency in one document but not the entire corpus documents. These words are usually specific to a discipline. For example, a document related to geography will have terms like topography, latitude, longitude, etc. But the same will not be true for a computer science document, which will likely have terms like data, processor, software, etc.
5. Keyword Extraction
People who read extensively intuitively develop skimming skills. They literally skim through a text – be it a newspaper, a magazine, or a book – by skipping out the insignificant words while holding on to the ones that matter the most. Thus, they can extract the meaning of a text without much ado.
Keyword extraction as NLP techniques does the same thing by finding the important words in a document. Therefore, keyword extraction is a text analysis technique that derives purposeful insights for any given topic. Thus, you don’t have to spend a lot of time reading through a document. You can simply use the keyword extraction technique to extract relevant keywords.
This technique is handy for NLP applications that wish to unearth customer feedback or identify the important points in any news item. There are two ways to do this:
- One is via TF-IDF, as discussed earlier. You can easily extract the top keyword using the highest TF-IDF.
- The second way to do keyword extraction is to use Gensim, an open-source Python library used for document indexing, topic modeling, etc. You can also use SpaCy and YAKE for keyword extraction.
6. Word Embeddings
An important question that confronts NLP data scientists is how to convert a body of text into numerical values that can be fed to machine learning and deep learning algorithms. Data scientists turn to word embeddings, also known as word vectors, to solve this issue.
Word embeddings refer to an approach whereby text and documents are represented using numeric vectors. It represents individual words as real-valued vectors in a lower-dimensional space. Similar words have similar representations.
In other words, it is a method that extracts the features of a text to enable us to input them into machine learning models. Hence, word embeddings are necessary for training a machine learning model.
You can use predefined word embeddings or learn them from scratch for a dataset. Various word embeddings are available today, including GloVe, TF-IDF, Word2Vec, BERT, ELMO, CountVectorizer, etc.
7. Sentiment Analysis
Sentiment analysis is an NLP technique used to contextualize a text to ascertain whether it is positive, negative, or neutral. It is also known as opinion mining and edge AI. Businesses employ this NLP technique to classify text and determine customer sentiment around their product or service.
It is also widely used by social media networks like Facebook and Twitter to curb hate speech and other objectionable content.
8. Topic Modeling
A topic model in natural language processing refers to a statistical model used to pull abstract topics or hidden themes from a collection of multiple documents. It is an unsupervised machine learning algorithm, which means it does not need training. Moreover, it makes it an easy and quick way to analyze data.
Companies use topic modeling to identify topics in customer reviews by finding recurring words and patterns. So, instead of spending hours sifting through tons of customer feedback data, you can use topic modeling to decipher the most essential topics quickly. This enables businesses to provide better customer service and improve their brand reputation.
9. Text Summarization
The text summarization technique of NLP is used to summarize a text and make it more concise while maintaining its coherence and fluency. It enables you to extract important information from a document without having to read every word of it. In other words, this automatic summarization saves you a lot of time.
There are two text summarization techniques.
- Extraction-based summarization – This technique does not entail making any changes to the original text. Instead, it just extracts some keywords and phrases from the document.
- Abstraction-based summarization – This summarization technique creates new phrases and sentences from the original document that depicts the most important information. It paraphrases the original document, thus changing the structure of sentences. Moreover, it also helps manage the grammatical errors or inconsistencies associated with the extraction-based summarization technique using AI tools.
10. Named Entity Recognition
Named Entity Recognition (NER) is a subfield of information extraction that manages the location and classification of named entities in an unstructured text and turns it into predefined categories. These categories include names of persons, dates, events, locations, etc.
NER is, by and large much like keyword extraction, except that it puts extracted keywords in predefined categories. So you can consider NER an extension of keyword extraction in that it takes it one step ahead. SpaCy offers built-in capabilities to carry out NER.
Summing it up
NLP techniques, like tokenization, stemming, lemmatization, and stop word removal, are used in all-natural language processing applications based on artificial intelligence. They fall under the domain of preprocessing. Similarly, keyword extraction, TF-IDF, and text summarization are helpful when analyzing texts. But these techniques also serve as the cornerstone of NLP model training.
To grow professionally, every data scientist should be proficient in these top 10 NLP techniques.
If you want to deploy an NLP application, contact us at firstname.lastname@example.org.