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Scalability in Perception for Autonomous Driving: Waymo Open Dataset
This work introduces a new large scale, high quality, diverse dataset, consisting of well synchronized and calibrated high quality LiDAR and camera data captured across a range of urban and suburban geographies, and studies the effects of dataset size and generalization across geographies on 3D detection methods.
Harnessing Context Incongruity for Sarcasm Detection
A computational system that harnesses context incongruity as a basis for sarcasm detection is presented and it is shown how the features can capture intersentential incongrouity.
A Fall-back Strategy for Sentiment Analysis in Hindi: a Case Study
Sentiment Analysis (SA) research has gained tremendous momentum in recent times. However, there has been little work in this area for an Indian language. We propose in this paper a fall-back strategy
Automatic Sarcasm Detection
Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text.
Are Word Embedding-based Features Useful for Sarcasm Detection?
A comparison of the four embeddings shows that Word2Vec and dependency weight-based features outperform LSA and GloVe, in terms of their benefit to sarcasm detection.
Automatic Sarcasm Detection: A Survey
This paper is the first known compilation of past work in automatic sarcasm detection, observing three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and use of context beyond target text.
How Do Cultural Differences Impact the Quality of Sarcasm Annotation?: A Case Study of Indian Annotators and American Text
This study considers the case of American text and Indian annotators, and shows that disagreements between annotators can be predicted using textual properties, and forms a stepping stone towards systematic evaluation of quality of these datasets annotated by non-native annotators.
Your Sentiment Precedes You: Using an author’s historical tweets to predict sarcasm
This work presents the first quantitative evidence to show that historical tweets by an author can provide additional context for sarcasm detection, and uses a contrast-based and historical tweet-based approach.
Political Issue Extraction Model: A Novel Hierarchical Topic Model That Uses Tweets By Political And Non-Political Authors
A Political Issue Extraction model that is capable of discovering political issues and positions from an unlabeled dataset of tweets is presented, and Estimated distributions are used to predict political affiliation with 68% accuracy.