Browsing Research Institutes and Centres by Author "QasemiZadeh, Behrang"
Now showing items 1-12 of 12
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The ACL RD-TEC: A Dataset for Benchmarking Terminology Extraction and Classification in Computational Linguistics
QasemiZadeh, Behrang; Handschuh, Siegfried (2014)This paper introduces ACL RD-TEC: a dataset for evaluating the extraction and classification of terms from literature in the domain of computational linguistics. The dataset is derived from the Association for Computational ... -
The ACL RD-TEC: Annotation Guideline (Ver 1.0)
QasemiZadeh, Behrang (Insight Centre for Data Analytics, 2014)Annotation Guidelines for the ACL RD-TEC (ver 1.0) is set out in this document. The annotator is required to understand the meaning of term, technology term, and invalid term before commencing the annotation task. A de ... -
Developing a Dataset for Technology Structure Mining
QasemiZadeh, Behrang; Buitelaar, Paul; Monaghan, Fergal (IEEE, 2010)This paper describes steps that have been taken to construct a development dataset for the task of Technology Structure Mining. We have defined the proposed task as the process of mapping a scientific corpus into a ... -
Evaluation of Technology Term Recognition with Random Indexing
QasemiZadeh, Behrang; Handschuh, Siegfried (2014)In this paper, we propose a method that combines the principles of automatic term recognition and the distributional hypothesis to identify technology terms from a corpus of scientific publications. We employ the ... -
Investigating Context Parameters in Technology Term Recognition
QasemiZadeh, Behrang; Handschuh, siegfried (2014)We propose and evaluate the task of technology term recognition: a method to extract technology terms at a synchronic level from a corpus of scientific publications. The proposed method is built on the principles of ... -
Random Indexing Explained with High Probability
QasemiZadeh, Behrang (2015)Random indexing (RI) is an incremental method for constructing a vector space model (VSM) with a reduced dimensionality. Previously, the method has been justified using the mathematical framework of Kanerva's sparse ... -
Random indexing revisited
QasemiZadeh, Behrang (Springer, 2015-05-17)Random indexing is a method for constructing vector spaces at a reduced dimensionality. Previously, the method has been proposed using Kanerva's sparse distributed memory model. Although intuitively plausible, this ... -
Random Manhattan Indexing
QasemiZadeh, Behrang; Handschuh, Siegfried (2014)Vector space models (VSMs) are mathematically well-defined frameworks that have been widely used in text processing. In these models, high-dimensional, often sparse vectors represent text units. In an application, the ... -
Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction
QasemiZadeh, Behrang; Handschuh, Siegfried (2014)Vector space models (VSMs) are mathematically well-defined frameworks that have been widely used in the distributional approaches to semantics. In VSMs, high-dimensional vectors represent linguistic entities. In an ... -
Semi-Supervised Technical Term Tagging With Minimal User Feedback
Buitelaar, Paul; Bordea, Georgeta; QasemiZadeh, Behrang (2012)In this paper, we address the problem of extracting technical terms automatically from an unannotated corpus. We introduce a technology term tagger, that is based on Liblinear Support Vector Machines and employs linguistic ... -
A Speech Based Approach to Surveillance Video Retrieval
QasemiZadeh, Behrang; O'Neill, Ian; Shen, Jiali; Miller, Paul; Hanna, Philip; Stwart, Darryl; Wang, Hongbin (2009)This paper describes the anatomy of a pilot surveillance system with a speech-based interface for content-based retrieval of video data. The proposed system relies on an ontology-based information sharing architecture and ... -
Towards Technology Structure Mining from Scientific Literature
QasemiZadeh, Behrang (Springer Berlin Heidelberg, 2010)This paper introduces the task of Technology-Structure Mining to support Management of Technology. We propose a linguistic based approach for identification of Technology Interdependence through extraction of technology ...