89,354 research outputs found
A Study on Agreement in PICO Span Annotations
In evidence-based medicine, relevance of medical literature is determined by
predefined relevance conditions. The conditions are defined based on PICO
elements, namely, Patient, Intervention, Comparator, and Outcome. Hence, PICO
annotations in medical literature are essential for automatic relevant document
filtering. However, defining boundaries of text spans for PICO elements is not
straightforward. In this paper, we study the agreement of PICO annotations made
by multiple human annotators, including both experts and non-experts.
Agreements are estimated by a standard span agreement (i.e., matching both
labels and boundaries of text spans), and two types of relaxed span agreement
(i.e., matching labels without guaranteeing matching boundaries of spans).
Based on the analysis, we report two observations: (i) Boundaries of PICO span
annotations by individual human annotators are very diverse. (ii) Despite the
disagreement in span boundaries, general areas of the span annotations are
broadly agreed by annotators. Our results suggest that applying a standard
agreement alone may undermine the agreement of PICO spans, and adopting both a
standard and a relaxed agreements is more suitable for PICO span evaluation.Comment: Accepted in SIGIR 2019 (Short paper
Inducing a Semantically Annotated Lexicon via EM-Based Clustering
We present a technique for automatic induction of slot annotations for
subcategorization frames, based on induction of hidden classes in the EM
framework of statistical estimation. The models are empirically evalutated by a
general decision test. Induction of slot labeling for subcategorization frames
is accomplished by a further application of EM, and applied experimentally on
frame observations derived from parsing large corpora. We outline an
interpretation of the learned representations as theoretical-linguistic
decompositional lexical entries.Comment: 8 pages, uses colacl.sty. Proceedings of the 37th Annual Meeting of
the ACL, 199
Geospatial Standardized Services for Integration of Weather Data Coming From Public and Voluntary Stations
The observation of the meteorological phenomena has evolved over the time time in the course of continuous technological change:
- Visual reading and manual annotations.
- The use of automated equipment which register and transmit observations.
- The implementation of remote sensing - Algorithms that allow defining weather values.
At the same time, all of those processes are some of the actual meteorological data sources.
Despite of this, it is well known that the best weather values at surface are observed by stations located on earth, but this type of observations have the inconvenience of a low geographic distribution.
An alternative information source for climate values at ground level could be the Volunteer Weather Observations (VWO) Networks. In Spain, we have detected several VWO networks covering an important area. But all of them have their own several features which imply complexity at working with all networks at the same time
Learning Models for Following Natural Language Directions in Unknown Environments
Natural language offers an intuitive and flexible means for humans to
communicate with the robots that we will increasingly work alongside in our
homes and workplaces. Recent advancements have given rise to robots that are
able to interpret natural language manipulation and navigation commands, but
these methods require a prior map of the robot's environment. In this paper, we
propose a novel learning framework that enables robots to successfully follow
natural language route directions without any previous knowledge of the
environment. The algorithm utilizes spatial and semantic information that the
human conveys through the command to learn a distribution over the metric and
semantic properties of spatially extended environments. Our method uses this
distribution in place of the latent world model and interprets the natural
language instruction as a distribution over the intended behavior. A novel
belief space planner reasons directly over the map and behavior distributions
to solve for a policy using imitation learning. We evaluate our framework on a
voice-commandable wheelchair. The results demonstrate that by learning and
performing inference over a latent environment model, the algorithm is able to
successfully follow natural language route directions within novel, extended
environments.Comment: ICRA 201
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