13,140 research outputs found
SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role Labeling
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 what?". Recent neural approaches do
not outperform the state-of-the-art feature-based models for Opinion Role
Labeling (ORL). We suspect this is due to the scarcity of labeled training data
and address this issue using different multi-task learning (MTL) techniques
with a related task which has substantially more data, i.e. Semantic Role
Labeling (SRL). We show that two MTL models improve significantly over the
single-task model for labeling of both holders and targets, on the development
and the test sets. We found that the vanilla MTL model which makes predictions
using only shared ORL and SRL features, performs the best. With deeper analysis
we determine what works and what might be done to make further improvements for
ORL.Comment: Published in NAACL 201
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
Despite the recent advances in opinion mining for written reviews, few works
have tackled the problem on other sources of reviews. In light of this issue,
we propose a multi-modal approach for mining fine-grained opinions from video
reviews that is able to determine the aspects of the item under review that are
being discussed and the sentiment orientation towards them. Our approach works
at the sentence level without the need for time annotations and uses features
derived from the audio, video and language transcriptions of its contents. We
evaluate our approach on two datasets and show that leveraging the video and
audio modalities consistently provides increased performance over text-only
baselines, providing evidence these extra modalities are key in better
understanding video reviews.Comment: Second Grand Challenge and Workshop on Multimodal Language ACL 202
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