125,975 research outputs found
Event-based Access to Historical Italian War Memoirs
The progressive digitization of historical archives provides new, often
domain specific, textual resources that report on facts and events which have
happened in the past; among these, memoirs are a very common type of primary
source. In this paper, we present an approach for extracting information from
Italian historical war memoirs and turning it into structured knowledge. This
is based on the semantic notions of events, participants and roles. We evaluate
quantitatively each of the key-steps of our approach and provide a graph-based
representation of the extracted knowledge, which allows to move between a Close
and a Distant Reading of the collection.Comment: 23 pages, 6 figure
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
Time Aware Knowledge Extraction for Microblog Summarization on Twitter
Microblogging services like Twitter and Facebook collect millions of user
generated content every moment about trending news, occurring events, and so
on. Nevertheless, it is really a nightmare to find information of interest
through the huge amount of available posts that are often noise and redundant.
In general, social media analytics services have caught increasing attention
from both side research and industry. Specifically, the dynamic context of
microblogging requires to manage not only meaning of information but also the
evolution of knowledge over the timeline. This work defines Time Aware
Knowledge Extraction (briefly TAKE) methodology that relies on temporal
extension of Fuzzy Formal Concept Analysis. In particular, a microblog
summarization algorithm has been defined filtering the concepts organized by
TAKE in a time-dependent hierarchy. The algorithm addresses topic-based
summarization on Twitter. Besides considering the timing of the concepts,
another distinguish feature of the proposed microblog summarization framework
is the possibility to have more or less detailed summary, according to the
user's needs, with good levels of quality and completeness as highlighted in
the experimental results.Comment: 33 pages, 10 figure
Dating Texts without Explicit Temporal Cues
This paper tackles temporal resolution of documents, such as determining when
a document is about or when it was written, based only on its text. We apply
techniques from information retrieval that predict dates via language models
over a discretized timeline. Unlike most previous works, we rely {\it solely}
on temporal cues implicit in the text. We consider both document-likelihood and
divergence based techniques and several smoothing methods for both of them. Our
best model predicts the mid-point of individuals' lives with a median of 22 and
mean error of 36 years for Wikipedia biographies from 3800 B.C. to the present
day. We also show that this approach works well when training on such
biographies and predicting dates both for non-biographical Wikipedia pages
about specific years (500 B.C. to 2010 A.D.) and for publication dates of short
stories (1798 to 2008). Together, our work shows that, even in absence of
temporal extraction resources, it is possible to achieve remarkable temporal
locality across a diverse set of texts
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