154,693 research outputs found
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
Convolutional neural network (CNN) is a neural network that can make use of
the internal structure of data such as the 2D structure of image data. This
paper studies CNN on text categorization to exploit the 1D structure (namely,
word order) of text data for accurate prediction. Instead of using
low-dimensional word vectors as input as is often done, we directly apply CNN
to high-dimensional text data, which leads to directly learning embedding of
small text regions for use in classification. In addition to a straightforward
adaptation of CNN from image to text, a simple but new variation which employs
bag-of-word conversion in the convolution layer is proposed. An extension to
combine multiple convolution layers is also explored for higher accuracy. The
experiments demonstrate the effectiveness of our approach in comparison with
state-of-the-art methods
Contextualised Browsing in a Digital Library's Living Lab
Contextualisation has proven to be effective in tailoring \linebreak search
results towards the users' information need. While this is true for a basic
query search, the usage of contextual session information during exploratory
search especially on the level of browsing has so far been underexposed in
research. In this paper, we present two approaches that contextualise browsing
on the level of structured metadata in a Digital Library (DL), (1) one variant
bases on document similarity and (2) one variant utilises implicit session
information, such as queries and different document metadata encountered during
the session of a users. We evaluate our approaches in a living lab environment
using a DL in the social sciences and compare our contextualisation approaches
against a non-contextualised approach. For a period of more than three months
we analysed 47,444 unique retrieval sessions that contain search activities on
the level of browsing. Our results show that a contextualisation of browsing
significantly outperforms our baseline in terms of the position of the first
clicked item in the result set. The mean rank of the first clicked document
(measured as mean first relevant - MFR) was 4.52 using a non-contextualised
ranking compared to 3.04 when re-ranking the result lists based on similarity
to the previously viewed document. Furthermore, we observed that both
contextual approaches show a noticeably higher click-through rate. A
contextualisation based on document similarity leads to almost twice as many
document views compared to the non-contextualised ranking.Comment: 10 pages, 2 figures, paper accepted at JCDL 201
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