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  • Shenghui Wang

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Deep-interactions with Open Collections (video)

OCLC Research Mini-symposium Part 3 - Deep-interactions with Open Collections (video)

By Rob Koopman, Shenghui Wang, Thom Castermans, Annapaola Ginammi

As the volume of digitized heritage collections continues to grow, memory institutions are challenged to making this open content discoverable and usable across repositories. At this mini-symposium in Leiden, guests learn about research & development work done in the area of digital image interoperability (IIIF), corpus-building and deep interactions with open collections by OCLC, and latest developments at Europeana and the Global Digitised Dataset Network

File: video, 44 minutes   Topics: Open Access, Semantic Embedding

Fast and Discriminative Semantic Embedding

Fast and Discriminative Semantic Embedding

By Rob Koopman, Shenghui Wang, and Gwenn Englebienne

13th International Conference on Computational Semantics
Gothenburg, Sweden

We present a novel, effective and efficient method for term and document embedding method. Our experiments show it outperforms state-of-the-art methods in terms of the STS benchmark and subject prediction when trained on the same datasets, while at the same time being computationally cheaper by orders of magnitude.

 

File: pptx, 4MB   Topics: Semantic Embedding

An Innovative Approach to Scalable Semantic Embedding

An Innovative Approach to Scalable Semantic Embedding

By Shenghui Wang, Rob Koopman

AIDR 2019: Artificial Intelligence for Data Discovery and Reuse
Pittsburgh, Pennsylvania, USA

Semantic search, in addition to keyword based search, is a desirable feature for many digital library systems. Even in the largely structured library data world, there is still a lot of tacit information locked in the free-text fields. Embedding words and texts in compact, semantically meaningful vector spaces allows for computable semantic similarity/relatedness which would make search more intelligent.

File: pptx, 4MB   Topics: Semantic Embedding

Subject Prediction Using Semantic Embedding

Subject Prediction Using Semantic Embedding

By Rob Koopman and Shenghui Wang

DANS colloquium 'Revisiting the NARCIS Classification’
The Hague (Netherlands)

Koopman and Wang describe semantic embedding and their work on the Ariadne random projection algorithm that attempts to predict the right mix of subject headings.

File: pdf   Topics: Semantic Embedding