7,169 research outputs found
On the Complexity of Case-Based Planning
We analyze the computational complexity of problems related to case-based
planning: planning when a plan for a similar instance is known, and planning
from a library of plans. We prove that planning from a single case has the same
complexity than generative planning (i.e., planning "from scratch"); using an
extended definition of cases, complexity is reduced if the domain stored in the
case is similar to the one to search plans for. Planning from a library of
cases is shown to have the same complexity. In both cases, the complexity of
planning remains, in the worst case, PSPACE-complete
The Evolution and Vitality of Merger Presumptions: A Decision-Theoretic Approach
This article reviews the formulation and evolution of the Philadelphia National Bank anticompetitive presumption through the lens of decision theory and Bayes Law. It explains how the economic theory, empirical evidence and experience are used to determine a presumption and how that presumption interacts with the reliability of relevant evidence to rationally set the appropriate burden of production and burden of persuasion to rebut the presumption. The article applies this reasoning to merger presumptions. It also sketches out a number of non-market share structural factors that might be used to supplement or replace the current legal and enforcement presumptions for mergers. It also discusses the potential for conflicting presumptions and how such conflicts might best be resolved
Structural Regularities in Text-based Entity Vector Spaces
Entity retrieval is the task of finding entities such as people or products
in response to a query, based solely on the textual documents they are
associated with. Recent semantic entity retrieval algorithms represent queries
and experts in finite-dimensional vector spaces, where both are constructed
from text sequences.
We investigate entity vector spaces and the degree to which they capture
structural regularities. Such vector spaces are constructed in an unsupervised
manner without explicit information about structural aspects. For concreteness,
we address these questions for a specific type of entity: experts in the
context of expert finding. We discover how clusterings of experts correspond to
committees in organizations, the ability of expert representations to encode
the co-author graph, and the degree to which they encode academic rank. We
compare latent, continuous representations created using methods based on
distributional semantics (LSI), topic models (LDA) and neural networks
(word2vec, doc2vec, SERT). Vector spaces created using neural methods, such as
doc2vec and SERT, systematically perform better at clustering than LSI, LDA and
word2vec. When it comes to encoding entity relations, SERT performs best.Comment: ICTIR2017. Proceedings of the 3rd ACM International Conference on the
Theory of Information Retrieval. 201
An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results
In Ordinal Classification tasks, items have to be assigned to classes that
have a relative ordering, such as positive, neutral, negative in sentiment
analysis. Remarkably, the most popular evaluation metrics for ordinal
classification tasks either ignore relevant information (for instance,
precision/recall on each of the classes ignores their relative ordering) or
assume additional information (for instance, Mean Average Error assumes
absolute distances between classes). In this paper we propose a new metric for
Ordinal Classification, Closeness Evaluation Measure, that is rooted on
Measurement Theory and Information Theory. Our theoretical analysis and
experimental results over both synthetic data and data from NLP shared tasks
indicate that the proposed metric captures quality aspects from different
traditional tasks simultaneously. In addition, it generalizes some popular
classification (nominal scale) and error minimization (interval scale) metrics,
depending on the measurement scale in which it is instantiated.Comment: To appear in Proceedings of ACL 202
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