Understanding Global Content

Understanding Global Content

Natural languages are the most intuitive medium for human-machine communication.  Our vision is to contribute to the understanding of the use of language in human thought and communication and thereby to achieve truly effective, frictionless human-human and human-machine interaction and collaboration through natural language.  To achieve this goal, computers should not only understand the physical world the speaker refers to, including the objects, relations, events, times, and spaces, but also understand the speakers’ minds as well, including the intentions, attitudes, sentiments and emotions.  The computer should be able to interact with humans using his/her native languages in the way of text, speech, image and video, including helping the user to find and extract information from the Internet, summarize that information, answer user's questions and take actions according to user's requests. This ability to be both informative and performative is a critical step forward.

Research team

Publications

Understanding Global Content

Exploring Online Novelty Detection Using First Story Detection Models

  • Posted: 9 Nov 2018
  • Author: Fei Wang, Robert Ross, John D. Kelleher
  • Publication: IDEAL 2018 - 19th International Conference on Intelligent Data Engineering and Automated Learning
Understanding Global Content

Using Word Semantics to Assist English as a Second Language Learners

  • Posted: 14 Mar 2015
  • Author: , Mahmoud Azab, Chris Hokamp and Rada Mihalcea
  • Publication: NAACL2015
Understanding Global Content

Re-evaluating Automatic Summarization with BLEU and 192 Shades of ROUGE

  • Posted: 1 Jul 2015
  • Author: , Yvette Graham
  • Publication: EMNLP 2015
Journal Article

Relating group size and posting activity of an online community of financial investors: Regularities and seasonal patterns

  • Posted: 3 Jan 2018
  • Author: , Paolo Racca, Roberto Casarin, Pierpaolo Dondio, Flaminio Squazzoni
  • Publication: Science Direct: Physica A: Statistical Mechanics and its Applications

Research Goals

We analyse, annotate and extract meaningful information and knowledge from textual content across multiple languages and domains. We develop a range of robust, domain-agnostic linguistic analysis tools, which can be applied to any language and which are informed by cues from non-linguistic sources.

We aim to develop the theories and technologies to understand the digital context through languages in the following three layers:

  • Understanding the language forms and structures, including the morphology, syntax, semantics and discourse.  This research focuses on the base language technologies by utilising the state-of-the-art machine learning and deep learning approaches to obtain cross-lingual, cross-domain and cross-modal content representations and improve the morphological, syntactic and semantic analysis performance for digital content.
  • Understanding the physical world through languages, including objects, relations, events, times, and spaces.  This research focuses on using representations for reasoning on language styles, events and topics. Researchers will advance machine learning techniques for content-based analysis by focusing on co-reference to entities and events at varying granularity, along with devising question-answering technology for events and novelty-detection technology for monitoring topics/events.
  • Understanding the human minds through languages, including the speaker's intentions, attitudes, the sentiments and emotions.  This research addresses the mechanics of variability in both the analysis and generation of digital content. Specific to this work is the notion that multiple layered meanings of words and word-phrases can vary not just because of the language, but the speaker, the medium and the context. Detecting changes in the meaning of words over time or by domain, assisting the disambiguation of phrases and terms, and the detection of meaning over larger structures than words alone.
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