The detection of expressions of sentiment in online text has become a popular Natural
Language Processing application. The task is commonly defined as identifying the words or …

We present a novel hierarchical distance-dependent Bayesian model for event coreference
resolution. While existing generative models for event coreference resolution are completely …

Opinion role labeling (ORL) is a fine-grained opinion analysis task and aims to answer “who
expressed what kind of sentiment towards what?”. Due to the scarcity of labeled data, ORL …

Fine-grained opinion mining (OM) has achieved increasing attraction in the natural
language processing (NLP) community, which aims to find the opinion structures of “Who …

For over a decade, machine learning has been used to extract opinion-holder-target
structures from text to answer the question" Who expressed what kind of sentiment towards …

Opinion object-attribute extraction is one of the fundamental tasks of fine-grained sentiment
analysis. It is accomplished by identifying opinion aspect entities (including object entities …

In this paper, we study the problem of aspect-based sentiment analysis. Our model
simultaneously extracts aspect-specific opinion expressions and determines the rating for …

Opinions are ubiquitous in text, and readers of online text—from consumers to sports fans to
news addicts to governments—can benefit from automatic methods that synthesize useful …

We propose a cross-lingual framework for fine-grained opinion mining using bitext
projection. The only requirements are a running system in a source language and word …

Opinionated expression extraction is a central problem in fine-grained sentiment analysis.
Most existing works focus on either generic subjective expression or aspect expression …