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

Fast Gated Neural Domain Adaptation: Language Model as a Case Study

  • Posted: 13 Dec 2016
  • Author: Andy Way, Jian Zhang, Xiaofeng Wu
  • Publication: COLING 2016
Understanding Global Content

Minority Language Twitter: Part-of-Speech Tagging and Analysis of Irish Tweets

  • Posted: 31 Jul 2015
  • Author: , Teresa Lynn, Kevin Scanell, Eimear Maguire
  • Publication: Conference: Workshop on Noisy User-generated Text (W-NUT 2015)
Understanding Global Content

Proceedings of MLP MomenT 2018: The Second Workshop on Multi-Language Processing in a Globalising World - The First Workshop on Multilingualism at the intersection of Knowledge Bases and Machine Translation

  • Posted: 5 Dec 2018
  • Author: Jinhua Du, Mihael Arcan, Qun Liu, Hitoshi Isahar
  • Publication: MLP 2018 - 2nd Workshop on Multi-Language Processing in a Globalising World, co-located with LREC 2018
Journal Article

Using a Socioeconomic Segreation Burn-in Model to Initialise and Agent-Based Model for Infectious Diseases

  • Posted: 31 Oct 2018
  • Author: John D. Kelleher, Elizabeth Hunter, Brian Mac Namee
  • Publication: Journal of Artificial Societies and Social Simulation

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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