585 research outputs found
F11RS SGR No. 13 (Fee Bill)
A RESOLUTION
To urge and request the Division of Finance and Administrative Services to provide a hyperlink to descriptions of each fee placed on the fee bill
Large Margin Nearest Neighbor Embedding for Knowledge Representation
Traditional way of storing facts in triplets ({\it head\_entity, relation,
tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively
displayed and easily acquired by mankind, but hardly computed or even reasoned
by AI machines. Inspired by the success in applying {\it Distributed
Representations} to AI-related fields, recent studies expect to represent each
entity and relation with a unique low-dimensional embedding, which is different
from the symbolic and atomic framework of displaying knowledge in triplets. In
this way, the knowledge computing and reasoning can be essentially facilitated
by means of a simple {\it vector calculation}, i.e. . We thus contribute an effective model to learn better embeddings
satisfying the formula by pulling the positive tail entities to
get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and
simultaneously pushing the negatives away from the positives
via keeping a {\it Large Margin}. We also design a corresponding
learning algorithm to efficiently find the optimal solution based on {\it
Stochastic Gradient Descent} in iterative fashion. Quantitative experiments
illustrate that our approach can achieve the state-of-the-art performance,
compared with several latest methods on some benchmark datasets for two
classical applications, i.e. {\it Link prediction} and {\it Triplet
classification}. Moreover, we analyze the parameter complexities among all the
evaluated models, and analytical results indicate that our model needs fewer
computational resources on outperforming the other methods.Comment: arXiv admin note: text overlap with arXiv:1503.0815
F11RS SGR No. 19 (TigerCASH Expansion)
A RESOLUTION
To urge and request the expansion of TigerCASH in the surrounding communit
Using Synchronic and Diachronic Relations for Summarizing Multiple Documents Describing Evolving Events
In this paper we present a fresh look at the problem of summarizing evolving
events from multiple sources. After a discussion concerning the nature of
evolving events we introduce a distinction between linearly and non-linearly
evolving events. We present then a general methodology for the automatic
creation of summaries from evolving events. At its heart lie the notions of
Synchronic and Diachronic cross-document Relations (SDRs), whose aim is the
identification of similarities and differences between sources, from a
synchronical and diachronical perspective. SDRs do not connect documents or
textual elements found therein, but structures one might call messages.
Applying this methodology will yield a set of messages and relations, SDRs,
connecting them, that is a graph which we call grid. We will show how such a
grid can be considered as the starting point of a Natural Language Generation
System. The methodology is evaluated in two case-studies, one for linearly
evolving events (descriptions of football matches) and another one for
non-linearly evolving events (terrorist incidents involving hostages). In both
cases we evaluate the results produced by our computational systems.Comment: 45 pages, 6 figures. To appear in the Journal of Intelligent
Information System
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