Detection of Stance-Related Characteristics in Social Media Text

In this paper, we present a study for the identification of stance-related features in text data from social media. Based on our previous work on stance and our findings on stance patterns, we detected stance-related characteristics in a data set from Twitter and Facebook. We extracted various corpus-, quantitative- and computational-based features that proved to be significant for six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge, and uncertainty), and we tested them in our data set. The results of a preliminary clustering method are presented and discussed as a starting point for future contributions in the field. The results of our experiments showed a strong correlation between different characteristics and stance constructions, which can lead us to a methodology for automatic stance annotation of these data

Improved Neural Relation Detection for Knowledge Base Question Answering

Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.Comment: Accepted by ACL 2017 (updated for camera-ready

Automatic keyphrase extraction: A survey of the state of the art

Abstract While automatic keyphrase extraction has been examined extensively, state-of-theart performance on this task is still much lower than that on many core natural language processing tasks. We present a survey of the state of the art in automatic keyphrase extraction, examining the major sources of errors made by existing systems and discussing the challenges ahead

Stance classification of ideological debates: Data, models, features, and constraints

Abstract Determining the stance expressed in a post written for a two-sided debate in an online debate forum is a relatively new and challenging problem in opinion mining. We seek to gain a better understanding of how to improve machine learning approaches to stance classification of ideological debates, specifically by examining how the performance of a learning-based stance classification system varies with the amount and quality of the training data, the complexity of the underlying model, the richness of the feature set, as well as the application of extra-linguistic constraints

Automatic keyphrase extraction: A survey of the state of the art

Abstract While automatic keyphrase extraction has been examined extensively, state-of-theart performance on this task is still much lower than that on many core natural language processing tasks. We present a survey of the state of the art in automatic keyphrase extraction, examining the major sources of errors made by existing systems and discussing the challenges ahead

Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates

Recent years have seen a surge of interest in stance classification in online debates. Oftentimes, however, it is important to de-termine not only the stance expressed by an author in her debate posts, but also the reasons behind her supporting or oppos-ing the issue under debate. We therefore examine the new task of reason classifi-cation in this paper. Given the close in-terplay between stance classification and reason classification, we design computa-tional models for examining how automat-ically computed stance information can be profitably exploited for reason classifica-tion. Experiments on our reason-annotated corpus of ideological debate posts from four domains demonstrate that sophisti-cated models of stances and reasons can indeed yield more accurate reason and stance classification results than their sim-pler counterparts.