Area of investment and support

Natural language processing

The exploration of computational techniques to learn, understand and produce human language content.

• Partners involved: Engineering and Physical Sciences Research Council (EPSRC)

The scope and what we're doing

Natural language processing is concerned with the exploration of computational techniques to learn, understand and produce human language content.

Natural language processing technologies can assist both human-human communication (for example, machine translation) and human-machine communication (for example, conversational interfaces and automated personal assistants), and can analyse and learn from the vast amount of textual data available online.

Natural language processing is important to the development of intelligent interfaces, to explainable artificial intelligence (AI), and to data science. This strategy notes the opportunities for increased activity and for maintaining our capability in mainstream statistical natural language processing within UK academia.

In parallel with a focus on data science and intelligent interfaces, we aim to maintain mainstream statistical natural language processing capability.

Development of new intelligent interfaces

We aim to have a research and training portfolio that contributes to development of new intelligent interfaces with natural language processing at their core.

Natural language processing will increasingly serve as an interface for communicating between humans and systems (for example, in the ‘internet of things’) and dialogue management will become increasingly important, linking natural language processing with the related fields of speech technologies and human-robot interaction.

Researchers should also be encouraged to address challenges in multi-modal interfaces (for example, by exploring and exploiting the links between language and vision).

Extracting knowledge from textual data

We aim to have a portfolio of research and training that includes work on enabling extraction of knowledge from large-scale textual data. The opportunity exists for researchers to target interdisciplinary work in this area, such as textual analytics enabling analysis of medical records.

Computing with meaning

We aim to have researchers working towards the goal of computing with meaning, contributing to the broad objective in AI of developing computational methods for ascribing semantics to human behaviours – for example, natural human interaction.

Highly skilled people

We will aim to have a supply of people with high-level skills, reflecting increasingly acute demand as natural language processing technologies are used in an increasing number of applications.

Researchers have the opportunity to play an important role in delivering the objectives of EPSRC’s Future Intelligent Technologies and Data-enabled Decision-making cross-ICT priorities, and are well-placed to contribute to the other cross-ICT priorities.

To maximise impact, they should ensure effective communication with researchers in areas such as AI Technologies, Graphics and Visualisation and Human-Computer Interaction.

Responsible innovation is a significant consideration. Researchers are encouraged to address issues of trust, identity and privacy with regard to how natural language processing is used in social contexts and large-scale social networks.

Why we're doing it

In view of the recent growth of the artificial intelligence (AI) technologies portfolio, in large part attributed to machine learning methods, it is clear that the research landscape in this area has changed significantly.

The UK has a particular strength in its depth of experience in combining natural language processing with machine learning methods. Natural language processing has been mentioned explicitly in the AI sector deal in relation to aiming to increase the AI workforce.

Natural language processing is also noted in the 2017 Hall and Pesenti report, ‘Growing the Artificial Intelligence Industry in the UK’, for “improving the resilience of UK industry from cyber attacks using active search, natural language processing and automated code and security integrity verification methods”.

A combination of factors – including the application of machine learning methods to vast amounts of linguistic data, and a significant increase in computing power – has led to advances in natural language processing and related technologies and this is likely to continue.

The UK has a small number of world-leading natural language processing research groups and is considered internationally competitive. It is therefore well-placed to capitalise on advances in this area, provided there is increased capacity to do so.

Capacity is currently low, but we wish to support the future success of the research base as demand for capability to create and integrate intelligent interfaces increases.

Industrial strength at the interface of speech and language technologies and machine learning is evidenced by the significant investment being made by major IT companies, including Amazon, Google and Apple, who have created or expanded UK-based research facilities and are heavily recruiting researchers with natural language processing expertise.

There have been several, high-profile UK start-up acquisitions (for example, Dark Blue Labs by Google, VocalIQ by Apple and SwiftKey by Microsoft) and significant growth in commercial interest in using natural language processing technologies (for example, in conversational interfaces and automated personal assistants).

There is a need to ensure a supply of people with high-level skills in natural language processing. Major IT companies are heavily recruiting staff with PhD and postdoctoral experience in natural language processing.

However, retention of expertise and key capacity in academia beyond PhD level is a recognised problem that will become even more acute as natural language processing and related technologies are used in an increasing number of applications.

Natural language processing is a significant research area for data science, where it enables management of unstructured data (for example, patient records, arts, heritage, literature, and the legal domain), and for robotics and autonomous systems, where natural language processings used for human-robot interaction in integrated systems is increasingly important.

In general terms, natural language processing is important to the health of related disciplines, such as human-computer interaction, robotics and AI, as both a driver of research and a user, collaborator, or magnifier for those disciplines’ research outputs.

View evidence sources used to inform our research strategies.

Past projects, outcomes and impact

Visualising our portfolio (VoP) is a tool for users to visually interact with the EPSRC portfolio and data relationships. Find out more about research area connections and funding for Natural Language Processing.

Find previously funded projects on Grants on the Web.

Last updated: 3 January 2023