Natural language processing (NLP) refers to a set of techniques that enable computers and people to interact. Most of the activities humans perform are done via language, whether communicated directly or delineated using natural language. Human language, developed over millennia, has become a nuanced form of communication that carries a wealth of information that […]
Natural language processing (NLP) refers to a set of techniques that enable computers and people to interact. Most of the activities humans perform are done via language, whether communicated directly or delineated using natural language. Human language, developed over millennia, has become a nuanced form of communication that carries a wealth of information that typically surpasses the words alone. As technology progressively makes the platforms and methods through which we communicate more accessible, the need to understand the languages we use to communicate becomes greater. By amalgamating the power of artificial intelligence (AI), computer science and computational linguistics, NLP helps machines ‘read’ text by mimicking the human ability to comprehend language.
The aspects that make a language natural is exactly what makes NLP difficult; the rules that dictate the representation of information in natural languages evolved without predetermination. These rules can be abstract and high level, like how sarcasm is used to denote meaning; or low level, like the use of the character ‘s’ to convey plurality of nouns. NLP involves identifying and making use of these rules with code to translate unstructured language data into information with a schema. There are still very challenging issues to solve in natural language. However, deep learning methods are accomplishing state of the art results in some specific language problems.
Early computational outlooks of language research focused on automating the analysis of the linguistic structure of language and creating basic technologies like machine translation, speech synthesis, and speech recognition. Today’s researchers hone and utilize such tools in real-world apps, creating speech-to-speech translation engines and spoken dialogue systems, identifying emotion and sentiment toward services and products, and mining social media information about finance or health.
While NLP may not be as mainstream as Machine Learning or Big Data, we utilize natural language apps or benefit from them on a daily basis. A 2017 report by Tractica on NLP estimated that the total NLP hardware, software and service market opportunity could reach $22.3 billion by 2025. The report also predicts that NLP based software solutions that leverage AI will record market growth from $136 million in 2016 to $5.4 billion by 2025. It’s quite clear that NLP is here to stay, and it's likely to have a larger impact on how humans interact with machines. Here are some examples of how NLP can change the way we collaborate in the enterprise.
Data classification simply refers to the process of organizing data by relevant categories so that it can be used and secured more efficiently. The classification process not only simplifies the retrieval of data, but also plays a crucial role in compliance, risk management, and data security. Data classification entails tagging data in order to make it trackable and searchable. It also curbs multiple duplications of data, which decreases backup and storage costs. Deep learning, which is used in natural language processing, is well suited for automated classification because it can learn the complex underlying structure of sentences and the semantic proximity of different words.
NLP classification algorithms can’t work ‘out -the-box’; they have to be trained to make specific predictions for texts. The algorithms are give a set of categorized/tagged text based on which the generate machine learning models, the models will then be able to automatically classify untagged text. Utilizing NLP to automate content classification makes the entire collaborative process efficient and fast.
The average enterprise generates massive amounts of data on a daily basis. In this digital age, information overload is a real phenomenon. Our access to information and knowledge has exceeded our capacity to make sense of it. When NLP is applied during the data ingestion, searchable indexes are automatically added to the document’s composition. In keyword based search, text and documents are searched based on the words found in the query. The returned results are typically based on the number of matches of the query words with documents.
In semantic search, the syntactic structure of the natural language, the frequency of the words and other linguistic elements are considered. An NLP algorithm can understand the specific requirements of the search query by identifying events, brands, people, places or phrases; understands how negative or positive the text is; and automatically curates a collection of results by topic. For easier discovery, personalized content recommendations can be generated related to the same topic.
Today’s workforce interacts with vast amounts of text all the time; constantly scrolling through files, and sharing documents. If they can extract intelligence from text, they will become more efficient and productive. Despite the fact that natural language processing is not a new science, the technology is rapidly advancing thanks to a growing interest in human-to-machine communications, powerful computing, enhanced algorithms, and the availability of big data. Utilizing Natural Language Processing (NLP) to create an interactive and smooth interface between machine and humans will continue being a top priority for increasingly cognitive applications.
