By Josh G
Natural Language Processing (NLP) falls under the umbrella of computer science, particularly artificial intelligence (AI), and is concerned with endowing computers with the ability to comprehend text and spoken language much like humans do. NLP employs a combination of computational linguistics, rule-based modeling of human language, and statistical, machine learning, and deep learning models. Using these technologies, computers can process text and voice data and comprehend the meaning, including the speaker or writer’s intent and sentiment. NLP powers computer programs that can translate text from one language to another, respond to spoken commands, and rapidly summarize large volumes of text, even in real time. NLP is present in various consumer conveniences, such as voice-operated GPS systems, digital assistants, speech-to-text dictation software, and customer service chatbots. However, NLP also plays a growing role in enterprise solutions that streamline business operations, increase employee productivity, and simplify mission-critical business processes.
Various NLP tasks involve breaking down a human text and voice data into smaller components to help computers comprehend the input. These tasks include:
Speech recognition, or speech-to-text, involves accurately converting spoken language into text. Speech recognition is necessary for any application that processes voice commands or answers spoken queries. The complexity of speech recognition arises from the way people speak — speaking quickly, blending words, using varying intonation and emphasis in different accents, and sometimes using incorrect grammar.
Part of speech tagging, or grammatical tagging, involves identifying the part of speech of a word or phrase based on the context in which it is used. For example, part of speech tagging distinguishes “make” as a verb in “I can make a paper plane” and as a noun in “What make of car do you own?”
Word sense disambiguation involves identifying the correct meaning of a word with multiple meanings by analyzing the context in which it is used. For instance, word sense disambiguation helps distinguish the sense of the verb “make” in “make the grade” (to succeed) versus “make a bet” (to place a bet).
Named entity recognition (NER) involves identifying words or phrases representing essential entities. NER identifies “Kentucky” as a location or “Fred” as a person’s name.
Co-reference resolution involves identifying when two words or phrases refer to the same entity, such as determining the referent of a pronoun (e.g., “she” refers to “Mary”) or identifying a metaphor or idiom in the text (e.g., a “bear” that represents a significant, hairy person).
Sentiment analysis involves extracting subjective qualities from the text, such as emotions, attitudes, sarcasm, confusion, or suspicion.
Natural language generation produces structured information in human language, sometimes seen as the opposite of speech recognition or speech-to-text.
NLP Tools and Approaches
The Natural Language Toolkit (NLTK) is an open-source collection of libraries, programs, and educational resources for building NLP programs. Python programming provides various tools and libraries for tackling specific NLP tasks, many of which are included in the NLTK. These libraries cover various NLP tasks such as sentence parsing, word segmentation, stemming and lemmatization, and tokenization. Additionally, NLTK provides libraries for implementing advanced capabilities such as semantic reasoning.
Initially, NLP applications were hand-coded, rules-based systems that were limited in scalability to accommodate prominent exceptions or increase text and voice data. However, statistical NLP came into existence, which combined computer algorithms with machine learning and deep learning models to automatically extract, classify, and label text and voice data elements. With learning techniques based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs), deep learning models enable NLP systems to ‘learn’ as they work and extract increasingly accurate meaning from massive volumes of raw, unstructured data and unlabeled text and voice data sets.
NLP Use Cases
Many modern real-world applications rely on natural language processing (NLP) for machine intelligence. Some examples include:
Spam detection: NLP’s text classification capabilities scan emails for language that often indicates spam or phishing. This includes overuse of financial terms, lousy grammar, threatening language, inappropriate urgency, and misspelled company names.
Machine translation: Google Translate is an example of widely available NLP technology at work. Effective translation involves accurately capturing the meaning and tone of the input language and translating it to text with the same meaning and impact in the output language. Machine translation tools are making progress in terms of accuracy.
Virtual agents and chatbots: Speech recognition and natural language generation are used to recognize patterns in voice commands or typed text entries and respond appropriately. The best of these also learn to recognize contextual clues over time.
Social media sentiment analysis: NLP analyzes language used in social media to extract attitudes and emotions in response to products, promotions, and events.
Text summarization: NLP techniques digest large volumes of digital text and create summaries or synopses with helpful context and conclusions.
In conclusion, natural language processing (NLP) has become an indispensable tool for machine intelligence in many modern real-world applications. From spam detection to machine translation, virtual agents and chatbots, social media sentiment analysis, and text summarization, NLP’s text classification, semantic reasoning, and natural language generation capabilities are helping to extract insights from unstructured text data and enable machines to understand and respond to human language. With the continued progress in machine learning and deep learning techniques, NLP is poised to play an even bigger role in shaping the future of human-machine interaction and unlocking the potential of big data.