Humans frequently experience emotions. When emotions arise, they affect not only our mental state but can also change our physical state. For example, we often open our eyes wide when we are surprised, or clap our hands when we feel excited. Physical manifestations of emotions are referred to as embodied emotion in the psychology literature. From an NLP perspective, recognizing descriptions of physical movements or physiological responses associated with emotions is a type of implicit emotion recognition. Our work introduces a new task of recognizing expressions of embodied emotion in natural language. We create a dataset of sentences that contains 7,300 body part mentions with human annotations for embodied emotion. We develop a classification model for this task and present two methods to acquire weakly labeled instances of embodied emotion by extracting emotional manner expressions and by prompting a language model. Our experiments show that the weakly labeled data can train an effective classification model without gold data, and can also improve performance when combined with gold data. Our dataset is publicly available at https://github.com/yyzhuang1991/Embodied-Emotions.

During crisis situations, observations of other people’s behaviors often play an essential role in a person’s decision-making. For example, a person might evacuate before a hurricane only if everyone else in the neighborhood does so. Conversely, a person might stay if no one else is leaving. Such observations are called social cues. Social cues are important for understanding people’s response to crises, so recognizing them can help inform the decisions of government officials and emergency responders. In this paper, we propose the first NLP task to categorize social cues in social media posts during crisis situations. We introduce a manually annotated dataset of 6,000 tweets, labeled with respect to eight social cue categories. We also present experimental results of several classification models, which show that some types of social cues can be recognized reasonably well, but overall this task is challenging for NLP systems. We further present error analyses to identify specific types of mistakes and promising directions for future research on this task.

Prior research on affective event classification showed that exploiting weakly labeled data for training can improve model performance. In this work, we propose a simpler and more effective approach for generating training data by automatically acquiring and labeling affective events with Multiple View Co-prompting, which leverages two language model prompts that provide independent views of an event. The approach starts with a modest amount of gold data and prompts pre-trained language models to generate new events. Next, information about the probable affective polarity of each event is collected from two complementary language model prompts and jointly used to assign polarity labels. Experimental results on two datasets show that the newly acquired events improve a state-of-the-art affective event classifier. We also present analyses which show that using multiple views produces polarity labels of higher quality than either view on its own.

Recently, neural models have been leveraged to significantly improve the performance of information extraction from semi-structured websites. However, a barrier for continued progress is the small number of datasets large enough to train these models. In this work, we introduce the PLAtE (Pages of Lists Attribute Extraction) benchmark dataset as a challenging new web extraction task. PLAtE focuses on shopping data, specifically extractions from product review pages with multiple items encompassing the tasks of: (1) finding product list segmentation boundaries and (2) extracting attributes for each product. PLAtE is composed of 52,898 items collected from 6,694 pages and 156,014 attributes, making it the first large-scale list page web extraction dataset. We use a multi-stage approach to collect and annotate the dataset and adapt three state-of-the-art web extraction models to the two tasks comparing their strengths and weaknesses both quantitatively and qualitatively.

Relation extraction models typically cast the problem of determining whether there is a relation between a pair of entities as a single decision. However, these models can struggle with long or complex language constructions in which two entities are not directly linked, as is often the case in scientific publications. We propose a novel approach that decomposes a binary relation into two unary relations that capture each argument’s role in the relation separately. We create a stacked learning model that incorporates information from unary and binary relation extractors to determine whether a relation holds between two entities. We present experimental results showing that this approach outperforms several competitive relation extractors on a new corpus of planetary science publications as well as a benchmark dataset in the biology domain.

Social media posts often contain questions, but many of the questions are rhetorical and do not seek information. Our work studies the problem of distinguishing rhetorical and information-seeking questions on Twitter. Most work has focused on features of the question itself, but we hypothesize that the prior context plays a role too. This paper introduces a new dataset containing questions in tweets paired with their prior tweets to provide context. We create classification models to assess the difficulty of distinguishing rhetorical and information-seeking questions, and experiment with different properties of the prior context. Our results show that the prior tweet and topic features can improve performance on this task.

Prior research has recognized the need to associate affective polarities with events and has produced several techniques and lexical resources for identifying affective events. Our research introduces new classification models to assign affective polarity to event phrases. First, we present a BERT-based model for affective event classification and show that the classifier achieves substantially better performance than a large affective event knowledge base. Second, we present a discourse-enhanced self-training method that iteratively improves the classifier with unlabeled data. The key idea is to exploit event phrases that occur with a coreferent sentiment expression. The discourse-enhanced self-training algorithm iteratively labels new event phrases based on both the classifier’s predictions and the polarities of the event’s coreferent sentiment expressions. Our results show that discourse-enhanced self-training further improves both recall and precision for affective event classification.