Natural Language Processing

A software that computationally understands human speech in text or audio format by evaluating the meaning and significance of words while completing tasks involving syntax, semantics, and discourse.
Technology Life Cycle

Technology Life Cycle


Sales growth slows as the market becomes saturated. The technology is well-established and competition peaks, leading to price drops and marginal improvements.

Technology Readiness Level (TRL)

Technology Readiness Level (TRL)

Fully Operative

Technology is operative and demonstrates considerable market competition among manufacturing industries.

Technology Diffusion

Technology Diffusion

Late Majority

Skeptical and adopts technology only after it has become mainstream and the benefits are well proven.

Natural Language Processing

Connecting resources from computer science, artificial intelligence and computational linguistics, natural language processing can capture natural human languages while evaluating the meaning and significance and completing tasks involving syntax, semantics, discourse, and speech. Human speech is not always precise, and often ambiguous; the linguistic structure can depend on complex variables with varying amounts of slang, dialects, and social context.

Nevertheless, technologies involving virtual assistants, automatic speech recognition, machine translation, question answering, and automatic text summarization have been improving drastically and even approaching human performance in some regards. In educational and training settings, this technology enables many professionals to gain access to these environments, for example, illiterate individuals and those with disabilities.

Deep neural networks are pushing the current boundaries of natural language processing, by encoding the semantic relationship between words into so-called word vectors. Word vector encodings can be learned automatically from large corpora of unstructured texts and have desirable properties such as being close if they have similar meaning and allowing semantic relationships between words to be expressed mathematically such as king – man + woman = queen without ever having been explicitly taught these.

By combining natural language processing and machine learning with the techniques of stylometry, the study of linguistic style rooted in the 15th century, it is possible to learn an author’s writing style and determine with increased accuracy which other texts have been written by the same person. This has already helped resolve long-standing disputes about the origin of certain historical texts and we have supposedly identified the author of the original Bitcoin white paper.

The applications of natural language processing are numerous as human language pervades almost every aspect of both online and offline life. In education, the content and cognitive complexity of a written essay could be automatically analyzed, undergo plagiarism detection, provide feedback and even give a score. Sentiment analysis is already being used for automated marketing campaigns, predictive trading, and news event classification.

Future Perspectives

The landscape of human-computer interactions is changing rapidly with natural language processing allowing data-informed design through conversational interfaces. Estimates claim unstructured data accounts for more than 90 percent of the digital universe, much of it coming in the form of text. With the exponential growth of data everywhere, humanity will depend on intelligent machines to extract, digest and present this data in meaningful ways. Applied to conversational robots and communication apps, the ability to naturally process human speech brings about a shift in technological development. Humans have been interacting with machines according to their language for decades now, but engineers and scientists are reverting this situation by teaching machines to understand human language and reproduce the same structure for more fluid and realistic interactions with users.

Automatic real-time translation could soon mean a world without language barriers. Imagine how much more productive we could be, or how many people we could learn from or talk to that we previously couldn’t. Without language barriers, the world opens up, especially to those who don’t have the privileges of first-world countries. The success of state-of-the-art neural language models which can represent meaning in the form of numerical vectors may allow us to wonder how the human brain processes language and help us understand the notion of consciousness, which is closely tied to semantic reasoning. If we can represent concepts numerically then maybe language becomes an implementation detail and we could choose to "render" a certain idea or concept to a language and writing style of our choice.

Image generated by Envisioning using Midjourney

When building applications for educational settings, Natural Language Processing (NLP) researchers should give serious consideration to the educational problem spaces for which an application is intended. This chapter discusses contexts in which NLP applications can support major educational problem spaces. The chapter first outlines these problem spaces, and then discusses how existing applications have addressed different aspects of each educational problem space, setting the stage for researchers to consider new opportunities.
Emma is a self-learning algorithm that monitors, analyses and understands How People Write.
Words and sentences embeddings have become an essential element of any Deep-Learning based Natural Language Processing system. They encode a word/sentence in a fixed-length vector.
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Directed by David Cage. With Valorie Curry, Bryan Dechart, Jesse Williams, Audrey Boustani. The game allows you to take control of three androids in their quest to discover who they really are.
Natural language processing (NLP) is the ability of a computer program to understand human speech as it is spoken. NLP makes it possible for an artifici...
The "Loving AI" project involves developing software enabling humanoid robots to interact with people in loving and compassionate ways, and to promote people' self-understanding and self-transcendence. Currently the project centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying Sophia with personality content and cognitive, linguistic, perceptual and behavioral content aimed at enabling loving interactions supportive of human self-transcendence. In September 2017 a small pilot study was conducted, involving the Sophia robot leading human subjects through dialogues and exercises focused on meditation, visualization and relaxation. The pilot was an apparent success, qualitatively demonstrating the viability of the approach and the ability of appropriate human-robot interaction to increase human well-being and advance human consciousness.
Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data. It extracts an author's identity and sociolinguistic characteristics based on the reflected writing styles in the text. It is an essential process for various areas, such as cybercrime investigation, psycholinguistics, political socialization, etc. However, most of the previous techniques critically depend on the manual feature engineering process. Consequently, the choice of feature set has been shown to be scenario- or dataset-dependent. In this paper, to mimic the human sentence composition process using a neural network approach, we propose to incorporate different categories of linguistic features into distributed representation of words in order to learn simultaneously the writing style representations based on unlabeled texts for authorship analysis. In particular, the proposed models allow topical, lexical, syntactical, and character-level feature vectors of each document to be extracted as stylometrics. We evaluate the performance of our approach on the problems of authorship characterization and authorship verification with the Twitter, novel, and essay datasets. The experiments suggest that our proposed text representation outperforms the bag-of-lexical-n-grams, Latent Dirichlet Allocation, Latent Semantic Analysis, PVDM, PVDBOW, and word2vec representations. (PDF) Learning Stylometric Representations for Authorship Analysis. Available from: [accessed Sep 23 2018].
The book is a state-of-the-art reference to one of the most active and productive fields in linguistics. It will be of interest and practical use to a wide range of linguists, as well as to researchers in such fields as informatics, artificial intelligence, language engineering, and cognitive science.
Many Internet services collect a flurry of data from their users. Privacy policies are intended to describe the services’ privacy practices. However, due to their length and complexity, reading privacy policies is a challenge for end users, government regulators, and companies. Natural language processing holds the promise of helping address this challenge. Specifically, we focus on comparing the practices described in privacy policies to the practices performed by smartphone apps covered by those policies. Government regulators are interested in comparing apps to their privacy policies in order to detect non-compliance with laws, and companies are interested for the same reason. We frame the identification of privacy practice statements in privacy policies as a classification problem, which we address with a three-tiered approach: a privacy practice statement is classified based on a data type (e.g., location), party (i.e., first or third party), and modality (i.e., whether a practice is explicitly described as being performed or not performed). Privacy policies omit discussion of many practices. With negative F1 scores ranging from 78% to 100%, the performance results of this three-tiered classification methodology suggests an improvement over the state-of-the-art. Our NLP analysis of privacy policies is an integral part of our Mobile App Privacy System (MAPS), which we used to analyze 1,035,853 free apps on the Google Play Store. Potential compliance issues appeared to be widespread, and those involving third parties were particularly common.
Analyze text to extract meta-data from content such as concepts, entities, keywords and more.
Directed by Stanley Kubrick. With Keir Dullea, Gary Lockwood, William Sylvester, Daniel Richter. Humanity finds a mysterious, obviously artificial object buried beneath the Lunar surface and, with the intelligent computer HAL 9000, sets off on a quest.
Learn about ETS research on the use of natural language processing technology to create educational applications that increase opportunities for classroom learning.

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