242 research outputs found
CNM: An Interpretable Complex-valued Network for Matching
This paper seeks to model human language by the mathematical framework of
quantum physics. With the well-designed mathematical formulations in quantum
physics, this framework unifies different linguistic units in a single
complex-valued vector space, e.g. words as particles in quantum states and
sentences as mixed systems. A complex-valued network is built to implement this
framework for semantic matching. With well-constrained complex-valued
components, the network admits interpretations to explicit physical meanings.
The proposed complex-valued network for matching (CNM) achieves comparable
performances to strong CNN and RNN baselines on two benchmarking question
answering (QA) datasets
Evaluation of information retrieval systems using structural equation modeling
The interpretation of the experimental data collected by testing systems across input datasets and model parameters is of strategic importance for system design and implementation. In particular, finding relationships between variables and detecting the latent variables affecting retrieval performance can provide designers, engineers and experimenters with useful if not necessary information about how a system is performing. This paper discusses the use of Structural Equation Modeling (SEM) in providing an in-depth explanation of evaluation results and an explanation of failures and successes of a system; in particular, we focus on the case of evaluation of Information Retrieval systems
Binary Classifier Inspired by Quantum Theory
Machine Learning (ML) helps us to recognize patterns from raw data. ML is
used in numerous domains i.e. biomedical, agricultural, food technology, etc.
Despite recent technological advancements, there is still room for substantial
improvement in prediction. Current ML models are based on classical theories of
probability and statistics, which can now be replaced by Quantum Theory (QT)
with the aim of improving the effectiveness of ML. In this paper, we propose
the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which
outperforms the state of the art classification in terms of recall for every
category.Comment: AAAI 201
Exploiting individual users and user groups interaction features: methodology and infrastructure design
Περιέχει το πλήρες κείμενοThe user may be a source of evidence for supporting infor-
mation access through Digital Library (DL) systems. In particular, the
features gathered while monitoring the interaction between the user and
a DL system can be used as implicit indicators of the user interests. How-
ever, each user has his own style of interaction and a feature which is a
reliable indicator with regard to one user may be no longer reliable when
referred to another user. This suggests the need to develop personalized
approaches for each user which are tailored for each search task. Never-
theless, the behavior of a group of interrelated users, e.g. performing the
same task, may improve the contribution provided by the personal be-
havior; for instance, some interaction features, if considered individually,
are more reliable with regard to a group of users. This paper introduces
a methodology for exploiting both the behavior of individual users and
group of users as sources of evidence. The paper also introduces a soft-
ware infrastructure implementing the methodology. The methodology is
mainly based on a geometric framework while the software infrastructure
is based on a partially decentralized Peer-To-Peer (P2P) network, thus
permitting the management of di erent sources of evidence
Improving Information Retrieval Effectiveness in Peer-to-Peer Networks through Query Piggybacking
Περιέχει το πλήρες κείμενοThis work describes an algorithm which aims at increasing
the quantity of relevant documents retrieved from a Peer-To-Peer (P2P)
network. The algorithm is based on a statistical model used for ranking
documents, peers and ultra-peers, and on a “piggybacking” technique
performed when the query is routed across the network. The algorithm
“amplifies” the statistical information about the neighborhood stored in
each ultra-peer. The preliminary experiments provided encouraging results
as the quantity of relevant documents retrieved through the network
almost doubles once query piggybacking is exploited
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Combining interaction and content for feedback-based ranking
The paper is concerned with the design and the evaluation of the combination of user interaction and informative content features for implicit and pseudo feedback-based document re-ranking. The features are observed during the visit of the top-ranked documents returned in response to a query. Experiments on a TREC Web test collection have been carried out and the experimental results are illustrated. We report that the effectiveness of the combination of user interaction for implicit feedback depends on whether document re-ranking is on a single-user or a user-group basis. Moreover, the adoption of document re-ranking on a user-group basis can improve pseudo-relevance feedback by providing more effective document for expanding queries
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