In this thesis proposal, we explore the application of event extraction to literary texts. Considering the lengths of literary documents modeling events in different granularities may be more adequate to extract meaningful information, as individual
elements contribute little to the overall semantics. We adapt the concept of schemas as sequences of events all describing a single process, connected through shared participants extending it to for multiple schemas in a document. Segmentation of event sequences into schemas is approached by modeling event sequences, on such task as the narrative cloze task, the prediction of missing events in sequences. We propose building on sequences of event embeddings to form schema embeddings, thereby summarizing sections of documents using a single representation. This approach will allow for the comparisons of different sections of documents and entire literary works. Literature is a challenging domain based on its variety of genres, yet the representation of literary content has received relatively little attention.
Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations. However, document-level event extraction is a challenging task as it requires the extraction of event and entity core
ference, and capturing arguments that span across different sentences. Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger. In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction. Experimental results show that our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.
Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencode
r, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction, but also converges more quickly.
The paper presents ongoing efforts in design of a typology of metacognitive events observed in a multimodal dialogue. The typology will serve as a tool to identify relations between participants' dispositions, dialogue actions and metacognitive indic
ators. It will be used to support an assessment of metacognitive knowledge, experiences and strategies of dialogue participants. Based on the mutidimensional dialogue model defined within the framework of Dynamic Interpretation Theory and ISO 24617-2 annotation standard, the proposed approach provides a systematic analysis of metacognitive events in terms of dialogue acts, i.e. concepts that dialogue research community is used to operate on in dialogue modelling and system design tasks.
We study the problem of Cross-lingual Event Argument Extraction (CEAE). The task aims to predict argument roles of entity mentions for events in text, whose language is different from the language that a predictive model has been trained on. Previous
work on CEAE has shown the cross-lingual benefits of universal dependency trees in capturing shared syntactic structures of sentences across languages. In particular, this work exploits the existence of the syntactic connections between the words in the dependency trees as the anchor knowledge to transfer the representation learning across languages for CEAE models (i.e., via graph convolutional neural networks -- GCNs). In this paper, we introduce two novel sources of language-independent information for CEAE models based on the semantic similarity and the universal dependency relations of the word pairs in different languages. We propose to use the two sources of information to produce shared sentence structures to bridge the gap between languages and improve the cross-lingual performance of the CEAE models. Extensive experiments are conducted with Arabic, Chinese, and English to demonstrate the effectiveness of the proposed method for CEAE.
Given a heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present
TENSORCAST, a novel method that forecasts time-evolving networks more accurately than the current state of the art methods by incorporating multiple data sources in coupled tensors. TENSORCAST is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with a different structure. We run our method on multiple real-world networks, including DBLP and a Twitter temporal network with over 310 million nonzeros, where we predict the evolution of the activity of the use of political hashtags.
It is clear that the
jurisdiction of juvenile court is stated in any crime committed by
the juvenile whether it is a traditional crime or a new crime such as
informatics crimes. Thus, a question arises about the importance of
the Syrian juvenile
Law No. 18 of 1974 which was amended by
Law 51 of 1979, to be applied on the informatics juvenile
delinquency in the absence of specialized judicial bodies concerned
with juvenile cases.
تعد الجريمة من الظواهر الاجتماعية الخطيرة التي وجدت بوجود الأنسان, و لايكمن خطرها في أشخاص المجرمين البالغين و أفعالهم فحسب, بل يكمن عند الصغار أيضا الذين لم يشتد عودهم بعد, وبحاجة إلى رعاية و عناية لينمو و يكبرو و يصبحو قادرين على المشاركة في بناء المجتمع و تقدمه.
The present study aimed at revealing The influence of the pressing
events on families during the current crises in Syrian and the
emergence of neuroticism between a high school students in Homs
. The sample of study consists of 200 students . The researcher has
used the following tools: The test of present events on families
during the current crises in Syrian, and the scale of neuroticism
from the junior Eysenck personality.
In this paper, we review related literature
and introduce a new general purpose simulation engine for
distributed discrete event simulation. We implemented optimized
loop CMB algorithms as a conservative algorithm in Akka
framework. The new engin
e is evaluated in terms of performance
and the ability of modeling and simulating discrete systems such
as digital circuits and single server queuing system.
