22,575 research outputs found
Linking Representations with Multimodal Contrastive Learning
Many applications require grouping instances contained in diverse document
datasets into classes. Most widely used methods do not employ deep learning and
do not exploit the inherently multimodal nature of documents. Notably, record
linkage is typically conceptualized as a string-matching problem. This study
develops CLIPPINGS, (Contrastively Linking Pooled Pre-trained Embeddings), a
multimodal framework for record linkage. CLIPPINGS employs end-to-end training
of symmetric vision and language bi-encoders, aligned through contrastive
language-image pre-training, to learn a metric space where the pooled
image-text representation for a given instance is close to representations in
the same class and distant from representations in different classes. At
inference time, instances can be linked by retrieving their nearest neighbor
from an offline exemplar embedding index or by clustering their
representations. The study examines two challenging applications: constructing
comprehensive supply chains for mid-20th century Japan through linking firm
level financial records - with each firm name represented by its crop in the
document image and the corresponding OCR - and detecting which image-caption
pairs in a massive corpus of historical U.S. newspapers came from the same
underlying photo wire source. CLIPPINGS outperforms widely used string matching
methods by a wide margin and also outperforms unimodal methods. Moreover, a
purely self-supervised model trained on only image-OCR pairs also outperforms
popular string-matching methods without requiring any labels
Robust Group Linkage
We study the problem of group linkage: linking records that refer to entities
in the same group. Applications for group linkage include finding businesses in
the same chain, finding conference attendees from the same affiliation, finding
players from the same team, etc. Group linkage faces challenges not present for
traditional record linkage. First, although different members in the same group
can share some similar global values of an attribute, they represent different
entities so can also have distinct local values for the same or different
attributes, requiring a high tolerance for value diversity. Second, groups can
be huge (with tens of thousands of records), requiring high scalability even
after using good blocking strategies.
We present a two-stage algorithm: the first stage identifies cores containing
records that are very likely to belong to the same group, while being robust to
possible erroneous values; the second stage collects strong evidence from the
cores and leverages it for merging more records into the same group, while
being tolerant to differences in local values of an attribute. Experimental
results show the high effectiveness and efficiency of our algorithm on various
real-world data sets
A Comparison of Blocking Methods for Record Linkage
Record linkage seeks to merge databases and to remove duplicates when unique
identifiers are not available. Most approaches use blocking techniques to
reduce the computational complexity associated with record linkage. We review
traditional blocking techniques, which typically partition the records
according to a set of field attributes, and consider two variants of a method
known as locality sensitive hashing, sometimes referred to as "private
blocking." We compare these approaches in terms of their recall, reduction
ratio, and computational complexity. We evaluate these methods using different
synthetic datafiles and conclude with a discussion of privacy-related issues.Comment: 22 pages, 2 tables, 7 figure
Application of k Means Clustering algorithm for prediction of Students Academic Performance
The ability to monitor the progress of students academic performance is a
critical issue to the academic community of higher learning. A system for
analyzing students results based on cluster analysis and uses standard
statistical algorithms to arrange their scores data according to the level of
their performance is described. In this paper, we also implemented k mean
clustering algorithm for analyzing students result data. The model was combined
with the deterministic model to analyze the students results of a private
Institution in Nigeria which is a good benchmark to monitor the progression of
academic performance of students in higher Institution for the purpose of
making an effective decision by the academic planners.Comment: IEEE format, International Journal of Computer Science and
Information Security, IJCSIS January 2010, ISSN 1947 5500,
http://sites.google.com/site/ijcsis
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