Browsing Data Science Institute (Conference Papers) by Author "QasemiZadeh, Behrang"

Now showing items 1-8 of 8

    • 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 ...

    • 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 ...

    • 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 ...

    • 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 ...