457 research outputs found
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
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Advances in statistical script learning
When humans encode information into natural language, they do so with the
clear assumption that the reader will be able to seamlessly make inferences
based on world knowledge. For example, given the sentence ``Mrs. Dalloway said
she would buy the flowers herself,'' one can make a number of probable
inferences based on event co-occurrences: she bought flowers, she went to a
store, she took the flowers home, and so on.
Observing this, it is clear that many different useful natural language
end-tasks could benefit from models of events as they typically co-occur
(so-called script models).
Robust question-answering systems must be able to infer highly-probable implicit
events from what is explicitly stated in a text, as must robust
information-extraction systems that map from unstructured text to formal
assertions about relations expressed in the text. Coreference resolution
systems, semantic role labeling, and even syntactic parsing systems could, in
principle, benefit from event co-occurrence models.
To this end, we present a number of contributions related to statistical
event co-occurrence models. First, we investigate a method of incorporating
multiple entities into events in a count-based co-occurrence model. We find that
modeling multiple entities interacting across events allows for improved
empirical performance on the task of modeling sequences of events in documents.
Second, we give a method of applying Recurrent Neural Network sequence models
to the task of predicting held-out predicate-argument structures from documents.
This model allows us to easily incorporate entity noun information, and can
allow for more complex, higher-arity events than a count-based co-occurrence
model. We find the neural model improves performance considerably over the
count-based co-occurrence model.
Third, we investigate the performance of a sequence-to-sequence encoder-decoder
neural model on the task of predicting held-out predicate-argument events from
text. This model does not explicitly model any external syntactic information,
and does not require a parser. We find the text-level model to be competitive in
predictive performance with an event level model directly mediated by an
external syntactic analysis.
Finally, motivated by this result, we investigate incorporating features derived
from these models into a baseline noun coreference resolution system. We find
that, while our additional features do not appreciably improve top-level
performance, we can nonetheless provide empirical improvement on a number of
restricted classes of difficult coreference decisions.Computer Science
Hypothesis Only Baselines in Natural Language Inference
We propose a hypothesis only baseline for diagnosing Natural Language
Inference (NLI). Especially when an NLI dataset assumes inference is occurring
based purely on the relationship between a context and a hypothesis, it follows
that assessing entailment relations while ignoring the provided context is a
degenerate solution. Yet, through experiments on ten distinct NLI datasets, we
find that this approach, which we refer to as a hypothesis-only model, is able
to significantly outperform a majority class baseline across a number of NLI
datasets. Our analysis suggests that statistical irregularities may allow a
model to perform NLI in some datasets beyond what should be achievable without
access to the context.Comment: Accepted at *SEM 2018 as long paper. 12 page
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization
Semantic specialization is the process of fine-tuning pre-trained
distributional word vectors using external lexical knowledge (e.g., WordNet) to
accentuate a particular semantic relation in the specialized vector space.
While post-processing specialization methods are applicable to arbitrary
distributional vectors, they are limited to updating only the vectors of words
occurring in external lexicons (i.e., seen words), leaving the vectors of all
other words unchanged. We propose a novel approach to specializing the full
distributional vocabulary. Our adversarial post-specialization method
propagates the external lexical knowledge to the full distributional space. We
exploit words seen in the resources as training examples for learning a global
specialization function. This function is learned by combining a standard
L2-distance loss with an adversarial loss: the adversarial component produces
more realistic output vectors. We show the effectiveness and robustness of the
proposed method across three languages and on three tasks: word similarity,
dialog state tracking, and lexical simplification. We report consistent
improvements over distributional word vectors and vectors specialized by other
state-of-the-art specialization frameworks. Finally, we also propose a
cross-lingual transfer method for zero-shot specialization which successfully
specializes a full target distributional space without any lexical knowledge in
the target language and without any bilingual data.Comment: Accepted at EMNLP 201
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