Natural language processing strives to build machines that understand and respond to text or voice data-and respond with text or speech of their own-in much the same way humans do.
Natural language processing (NLP) refers to the branch of computer science-and more specifically, the branch of artificial intelligence or AI-concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.
Common NLP Tasks
Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in sentence structure-these just a few of the irregularities of human language that take humans years to learn, but that programmers must teach natural language-driven applications to recognize and understand accurately from the start, if those applications are going to be useful. Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it’s ingesting. Some of these tasks include the following:
- Speech recognition: Also called speech-to-text, is the task of reliably converting voice data into text data. Speech recognition is required for any application that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the way people talk—quickly, slurring words together, with varying emphasis and intonation, in different accents, and often using incorrect grammar.
- Part of speech tagging: Also called grammatical tagging, is the process of determining the part of speech of a particular word or piece of text based on its use and context. Part of speech identifies ‘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: Is the selection of the meaning of a word with multiple meanings through a process of semantic analysis that determine the word that makes the most sense in the given context. For example, word sense disambiguation helps distinguish the meaning of the verb ‘make’ in ‘make the grade’ (achieve) vs. ‘make a bet’ (place).
- Named entity recognition: NEM identifies words or phrases as useful entities. NEM identifies ‘Kentucky’ as a location or ‘Fred’ as a man’s name.
- Co-reference resolution: Is the task of identifying if and when two words refer to the same entity. The most common example is determining the person or object to which a certain pronoun refers, but it can also involve identifying a metaphor or an idiom in the text.
- Sentiment analysis: Attempts to extract subjective qualities-attitudes, emotions, sarcasm, confusion, suspicion-from text.
- Natural language generation: Is sometimes described as the opposite of speech recognition or speech-to-text; it’s the task of putting structured information into human language.
- Content categorization: A linguistic-based document summary, including search and indexing, content alerts and duplication detection.
- Topic discovery and modeling: Accurately capture the meaning and themes in text collections, and apply advanced analytics to text, like optimization and forecasting.
- Corpus Analysis: Understand corpus and document structure through output statistics for tasks such as sampling effectively, preparing data as input for further models and strategizing modeling approaches.
- Contextual extraction: Automatically pull structured information from text-based sources.
- Speech-to-text and text-to-speech conversion: Transforming voice commands into written text, and vice versa.
- Document summarization: Automatically generating synopses of large bodies of text and detect represented languages in multi-lingual corpora (documents).
- Machine translation: Automatic translation of text or speech from one language to another.
- Text summarization: Text summarization uses NLP techniques to digest huge volumes of digital text and create summaries and synopses for indexes, research databases, or busy readers who don’t have time to read full text.
- Virtual agents and chatbots: Virtual agents such as Apple’s Siri and Amazon’s Alexa use speech recognition to recognize patterns in voice commands and natural language generation to respond with appropriate action or helpful comments.
- Spam detection: You may not think of spam detection as an NLP solution, but the best spam detection technologies use NLP’s text classification capabilities to scan emails and texts for language that often indicates spam or phishing.
NLP in Oil and Gas Industry
Oil and gas operations have many needs, most it is based around continuous operations with maximum efficiency and minimal safety risks. NLP, can help oil and gas accomplish all of these goals. So what does natural language processing do? Traditionally, machines could only use structured data that had been carefully curated into formats algorithms can easily understand and search, such as spreadsheets and cleanly organized sensor data. According to Oracle, structured data only accounts for about 20% of generated data. The rest is locked away in emails, journals, notes, audio, video, images, analog data, and more. These sorts of records are taken by businesses but rarely used, since they have previously been accessible only to humans, but NLP can change that. An application powered by NLP is capable of using and understanding unstructured data, unlocking a vast wealth of valuable information.
Oil and Gas companies store various kinds of data associated with their assets and operations (typically in databases) from which it can be retrieved by stakeholders as required. The process of retrieval and transformation of data for manual analytics can often be time-consuming and NLP can speed up this process. The information that can be extracted from unstructured data can include insights on well and reservoir planning, among many other critical details of production, which can be used to maximize operational efficiency in an entirely new way.
NLP can also make use of unstructured data critical to safe operations, such as injury reports. For example, a keyword search of the safety logs of a large utility company for “lower body injuries” resulted in 534 entries. A semantic search, which included ontologically related terms such as “leg,” “foot,” or “toe,” returned 1,027 results, many of which had nothing to do with actual injuries. A cognitive search using natural language processing, however, gave a far more accurate listing of 347 incidents, removing incorrect references to body parts, such as “foot” used as a unit of measurement, and focusing on references to body parts in the context of injuries.