2 research outputs found
Users' Traces for Enhancing Arabic Facebook Search
International audienceThis paper proposes an approach on Facebook search in Arabic, which exploits several users' traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (signals) can play a vital role in improving Arabic Facebook search. Firstly, we identify polarities (positive or negative) carried by the textual signals (e.g. comments) and non-textual ones (e.g. the reactions love and sad) for a given Facebook post. Therefore, the polarity of each comment expressed on a given Facebook post, is estimated on the basis of a neural sentiment model in Arabic language. Secondly, we group signals according to their complementarity using features selection algorithms. Thirdly, we apply learning to rank (LTR) algorithms to re-rank Facebook search results based on the selected groups of signals. Finally, experiments are carried out on 13,500 Facebook posts, collected from 45 topics in Arabic language. Experiments results reveal that Random Forests combined with ReliefFAttributeEval (RLF) was the most effective LTR approach for this task
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Event extraction is a fundamental task for natural language processing.
Finding the roles of event arguments like event participants is essential for
event extraction. However, doing so for real-life event descriptions is
challenging because an argument's role often varies in different contexts.
While the relationship and interactions between multiple arguments are useful
for settling the argument roles, such information is largely ignored by
existing approaches. This paper presents a better approach for event extraction
by explicitly utilizing the relationships of event arguments. We achieve this
through a carefully designed task-oriented dialogue system. To model the
argument relation, we employ reinforcement learning and incremental learning to
extract multiple arguments via a multi-turned, iterative process. Our approach
leverages knowledge of the already extracted arguments of the same sentence to
determine the role of arguments that would be difficult to decide individually.
It then uses the newly obtained information to improve the decisions of
previously extracted arguments. This two-way feedback process allows us to
exploit the argument relations to effectively settle argument roles, leading to
better sentence understanding and event extraction. Experimental results show
that our approach consistently outperforms seven state-of-the-art event
extraction methods for the classification of events and argument role and
argument identification