6 research outputs found
Transition-based directed graph construction for emotion-cause pair extraction
Emotion-cause pair extraction aims to extract all potential pairs of emotions and corresponding causes from unannotated emotion text. Most existing methods are pipelined framework, which identifies emotions and extracts causes separately, leading to a drawback of error propagation. Towards this issue, we propose a transition-based model to transform the task into a procedure of parsing-like directed graph construction. The proposed model incrementally generates the directed graph with labeled edges based on a sequence of actions, from which we can recognize emotions with the corresponding causes simultaneously, thereby optimizing separate subtasks jointly and maximizing mutual benefits of tasks interdependently. Experimental results show that our approach achieves the best performance, outperforming the state-of-the-art methods by 6.71% (p<0.01) in F1 measure
Investigating the Relationship between Classification Quality and SMT Performance in Discriminative Reordering Models
Reordering is one of the most important factors affecting the quality of the output in
statistical machine translation (SMT). A considerable number of approaches that proposed addressing
the reordering problem are discriminative reordering models (DRM). The core component of the
DRMs is a classifier which tries to predict the correct word order of the sentence. Unfortunately,
the relationship between classification quality and ultimate SMT performance has not been
investigated to date. Understanding this relationship will allow researchers to select the classifier that
results in the best possible MT quality. It might be assumed that there is a monotonic relationship
between classification quality and SMT performance, i.e., any improvement in classification
performance will be monotonically reflected in overall SMT quality. In this paper, we experimentally
show that this assumption does not always hold, i.e., an improvement in classification performance
might actually degrade the quality of an SMT system, from the point of view of MT automatic
evaluation metrics. However, we show that if the improvement in the classification performance is
high enough, we can expect the SMT quality to improve as well. In addition to this, we show that
there is a negative relationship between classification accuracy and SMT performance in imbalanced
parallel corpora. For these types of corpora, we provide evidence that, for the evaluation of the
classifier, macro-averaged metrics such as macro-averaged F-measure are better suited than accuracy,
the metric commonly used to date
Sentiment Analysis for Social Media
Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection