Essential Natural Language Processing (NLP) Techniques

In today’s fast-paced digital world, the ability to communicate effectively with machines is becoming increasingly important. According to recent statistics, 57% of companies and businesses are already using machine learning, including Natural Language Processing (NLP) techniques, to improve consumer experience. Moreover, a large 49% of companies are leveraging NLP and AI for predictive analytics and data mining. This growth is driven by the increasing use of AI and machine learning (ML) in various industries, as well as the need to extract insights from large amounts of unstructured data.   In this blog by SoftmaxAI, we will explore the various Natural Language Processing (NLP) techniques used in machine learning. So, whether you are a data scientist, a business analyst, or just someone interested in AI and ML, this blog is for you! Let’s take a look into the fascinating world of Natural Language Processing (NLP) techniques.

What is Natural Language Processing (NLP)?

Natural language processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. NLP combines techniques from computer science, linguistics, and machine learning to build systems that can process and analyze large amounts of natural language data. In simple terms, NLP is what allows computers to communicate with humans in our own language, whether by speech or text. NLP enables computers to not just read text but to understand the meaning, intent, and sentiments behind the words.

List of Natural Language Processing (NLP) Techniques

Text Preprocessing Techniques

Tokenization is the process of breaking down raw text into smaller units called tokens, which are usually individual words or phrases. Tokenization helps split text into meaningful segments that can be easily processed by NLP models. For example, the sentence “I love natural language processing” could be tokenized into the tokens: “I”, “love”, “natural”, “language”, “processing”.  Sentence Segmentation, also known as sentence boundary detection, involves identifying the boundaries between sentences within a text. This is often done by splitting sentences based on punctuation like periods, question marks and exclamation points. Sentence segmentation is a crucial first step before performing further linguistic analysis on each individual sentence. Stemming refers to the process of reducing words to their word stem or root form. A stemming algorithm strips affixes from words, often including derivational affixes. For example, the words “jumping”, “jumped”, “jumps” would all be reduced to the stem “jump”. Stemming helps reduce the vocabulary size and improves the efficiency of NLP model. Lemmatization is the process of determining the dictionary form of a word, known as its lemma. Unlike stemming, lemmatization considers the morphological analysis of words to return the base or dictionary form. For example, the word “better” would be lemmatized to “good”. Lemmatization is more complex than stemming but results in more meaningful base words.  Part-of-Speech (POS) Tagging involves labeling each word in a text with its corresponding part of speech, such as noun, verb, adjective, etc. POS tagging helps understand the grammatical structure of sentences and enables further syntactic analysis. For example, in the sentence “The dog quickly chased the ball”, “The” would be tagged as a determiner, “dog” and “ball” as nouns, “quickly” as an adverb, and “chased” as a verb.  Morphological Segmentation is the process of splitting words into morphemes, the smallest units of meaning. For example, the word “unhappiness” could be segmented into three morphemes: “un-“, “happy”, “-ness”. Morphological segmentation helps understand the internal structure of words.  Stop Words Removal is a preprocessing technique that eliminates common words that appear frequently but add little semantic value, such as “a”, “an”, “the”, “in”, “on”, etc. Removing stop words helps reduce noise and improve the efficiency of NLP models by focusing on the most meaningful words.  

Core Natural Language Processing (NLP) Techniques

Named Entity Recognition (NER) is a fundamental  Natural Language Processing (NLP) technique that identifies and classifies named entities like people, places, organizations, and dates in unstructured text using machine learning algorithms. NER is crucial for extracting key information to enable various downstream NLP applications. Natural Language Processing Machine learning approaches commonly used for NER include Hidden Markov Models (HMMs), Conditional Random Fields (CRFs), and deep learning models. Sentiment Analysis, a key natural language processing (NLP) technique, determines the emotional tone (positive, negative, neutral) behind text using natural language processing and machine learning. It helps understand customer opinions, brand perception, and market trends by leveraging rule-based approaches, machine learning algorithms like Naive Bayes and Support Vector Machines (SVM), as well as deep learning models. Sentiment analysis enables computers to use language to process data and extract valuable insights. Text Summarization condenses long text into concise summaries while preserving key information by applying NLP and machine learning techniques. Extractive methods select important sentences, while abstractive techniques generate new text capturing the essence. Deep learning models like encoder-decoder networks and transformers have significantly advanced abstractive summarization capabilities in NLP. Pattern Recognition, a parsing technique in natural language processing, identifies recurring patterns and themes in unstructured text to enable knowledge discovery. It allows computers to use language to process data and extract meaningful patterns. Text Normalization is a crucial Natural Language Processing (NLP) technique preprocessing step that converts text to a standard format by removing noise, expanding abbreviations, and handling variations. Normalization improves the quality of natural language data before applying machine learning.

Advanced NLP Techniques

Coreference resolution, a crucial Natural Language Processing (NLP) technique, determines when different words refer to the same entity by leveraging machine learning approaches like mention-pair models and entity-centric models. This allows computers to establish coherence when using language to process data.  Parsing, a fundamental  Natural Language Processing (NLP) technique, analyzes sentence structure using techniques like top-down, bottom-up, and probabilistic parsing. By exposing syntactic relationships, parsing techniques in natural language processing enables machines to comprehend language at a deeper level.  Topic modeling, an unsupervised Natural Language Processing (NLP) technique, uncovers latent topics in document collections using methods like Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF). This allows computers to process unstructured text and extract meaningful insights. Keyword extraction, an important Natural Language Processing (NLP) technique, automatically identifies the most relevant words or phrases in a text using Natural language processing (NLP) in machine learning and linguistic approaches. By distilling key information, keyword extraction enables computers to understand the essence of textual data.

A variety of tools that use Natural Language Processing (NLP):

• Sentiment Analysis: A mobile carrier analyzing customer support requests to improve services
• Text Analysis from Emails, Reviews, Sales Call Transcripts: Analyzing customer reviews to identify product improvement areas
• Translation Tools: Microsoft Translator translating “Can you recommend a good Italian restaurant in the city?” into Italian
• Virtual Assistants: Mycroft AI, the first open-source voice assistant performs tasks or services based on voice commands, offering hands-free operation

Final Thoughts

As the memorable quote from the movie “Her” goes, “The past is just a story we tell ourselves.” Similarly, the future of NLP is a story yet to be written, but one that holds immense potential. With the rapid advancements in artificial intelligence and machine learning, we are on the cusp of a new era where computers can truly comprehend and communicate in natural language, blurring the lines between human and machine interaction (scary good future).   Have something we didn’t mention? Share it in the comments – we’d also love to hear how AI is supercharging your work! And don’t forget to follow SoftmaxAI on LinkedIn | Twitter | Facebook | Instagram for more game-changing AI insights.