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Ding

AAAI Conferences

To understand narrative text, we must comprehend how people are affected by the events that they experience. For example, readers understand that graduating from college is a positive event (achievement) but being fired from one's job is a negative event (problem). NLP researchers have developed effective tools for recognizing explicit sentiments, but affective events are more difficult to recognize because the polarity is often implicit and can depend on both a predicate and its arguments. Our research investigates the prevalence of affective events in a personal story corpus, and introduces a weakly supervised method for large scale induction of affective events. We present an iterative learning framework that constructs a graph with nodes representing events and initializes their affective polarities with sentiment analysis tools as weak supervision. The events are then linked based on three types of semantic relations: (1) semantic similarity, (2) semantic opposition, and (3) shared components.


Acquiring Knowledge of Affective Events from Blogs Using Label Propagation

AAAI Conferences

Many common events in our daily life affect us in positive and negative ways. For example, going on vacation is typically an enjoyable event, while being rushed to the hospital is an undesirable event. In narrative stories and personal conversations, recognizing that some events have a strong affective polarity is essential to understand the discourse and the emotional states of the affected people. However, current NLP systems mainly depend on sentiment analysis tools, which fail to recognize many events that are implicitly affective based on human knowledge about the event itself and cultural norms. Our goal is to automatically acquire knowledge of stereotypically positive and negative events from personal blogs. Our research creates an event context graph from a large collection of blog posts and uses a sentiment classifier and semi-supervised label propagation algorithm to discover affective events. We explore several graph configurations that propagate affective polarity across edges using local context, discourse proximity, and event-event co-occurrence. We then harvest highly affective events from the graph and evaluate the agreement of the polarities with human judgements.


Ding

AAAI Conferences

For example, going on vacation is typically an enjoyable event, while being rushed to the hospital is an undesirable event. In narrative stories and personal conversations, recognizing that some events have a strong affective polarity is essential to understand the discourse and the emotional states of the affected people. However, current NLP systems mainly depend on sentiment analysis tools, which fail to recognize many events that are implicitly affective based on human knowledge about the event itself and cultural norms. Our goal is to automatically acquire knowledge of stereotypically positive and negative events from personal blogs. Our research creates an event context graph from a large collection of blog posts and uses a sentiment classifier and semi-supervised label propagation algorithm to discover affective events. We explore several graph configurations that propagate affective polarity across edges using local context, discourse proximity, and event-event co-occurrence. We then harvest highly affective events from the graph and evaluate the agreement of the polarities with human judgements.


[Editors' Choice] Polarity reversal during tissue remodeling

Science

The epithelial-mesenchymal transition occurs when epithelial cells lose apicobasal polarity and cell-cell contacts and migrate into surrounding tissues as mesenchymal cells. Migration is crucial for gastrulation, neural tube formation, and cancer metastasis. However, the mechanisms underlying loss of polarity and cell movement are poorly understood. The change in cell organization results in the transition from apicobasal polarity to front-rear polarity that precedes cell migration. Micropatterned cell culture showed that the mechanism is cell-intrinsic and governed by microtubule reorganization but is not influenced by neighboring cells.


Yoshida

AAAI Conferences

Sentiment analysis is the task of determining the attitude (positive or negative) of documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This paper proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains.