9,049 research outputs found
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
Every culture and language is unique. Our work expressly focuses on the
uniqueness of culture and language in relation to human affect, specifically
sentiment and emotion semantics, and how they manifest in social multimedia. We
develop sets of sentiment- and emotion-polarized visual concepts by adapting
semantic structures called adjective-noun pairs, originally introduced by Borth
et al. (2013), but in a multilingual context. We propose a new
language-dependent method for automatic discovery of these adjective-noun
constructs. We show how this pipeline can be applied on a social multimedia
platform for the creation of a large-scale multilingual visual sentiment
concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our
unified ontology is organized hierarchically by multilingual clusters of
visually detectable nouns and subclusters of emotionally biased versions of
these nouns. In addition, we present an image-based prediction task to show how
generalizable language-specific models are in a multilingual context. A new,
publicly available dataset of >15.6K sentiment-biased visual concepts across 12
languages with language-specific detector banks, >7.36M images and their
metadata is also released.Comment: 11 pages, to appear at ACM MM'1
A Machine Learning Approach For Opinion Holder Extraction In Arabic Language
Opinion mining aims at extracting useful subjective information from reliable
amounts of text. Opinion mining holder recognition is a task that has not been
considered yet in Arabic Language. This task essentially requires deep
understanding of clauses structures. Unfortunately, the lack of a robust,
publicly available, Arabic parser further complicates the research. This paper
presents a leading research for the opinion holder extraction in Arabic news
independent from any lexical parsers. We investigate constructing a
comprehensive feature set to compensate the lack of parsing structural
outcomes. The proposed feature set is tuned from English previous works coupled
with our proposed semantic field and named entities features. Our feature
analysis is based on Conditional Random Fields (CRF) and semi-supervised
pattern recognition techniques. Different research models are evaluated via
cross-validation experiments achieving 54.03 F-measure. We publicly release our
own research outcome corpus and lexicon for opinion mining community to
encourage further research
Cross-lingual Emotion Detection
Emotion detection is of great importance for understanding humans.
Constructing annotated datasets to train automated models can be expensive. We
explore the efficacy of cross-lingual approaches that would use data from a
source language to build models for emotion detection in a target language. We
compare three approaches, namely: i) using inherently multilingual models; ii)
translating training data into the target language; and iii) using an
automatically tagged parallel corpus. In our study, we consider English as the
source language with Arabic and Spanish as target languages. We study the
effectiveness of different classification models such as BERT and SVMs trained
with different features. Our BERT-based monolingual models that are trained on
target language data surpass state-of-the-art (SOTA) by 4% and 5% absolute
Jaccard score for Arabic and Spanish respectively. Next, we show that using
cross-lingual approaches with English data alone, we can achieve more than 90%
and 80% relative effectiveness of the Arabic and Spanish BERT models
respectively. Lastly, we use LIME to interpret the differences between models
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages
In a conventional Speech emotion recognition (SER) task, a classifier for a
given language is trained on a pre-existing dataset for that same language.
However, where training data for a language does not exist, data from other
languages can be used instead. We experiment with cross-lingual and
multilingual SER, working with Amharic, English, German and URDU. For Amharic,
we use our own publicly-available Amharic Speech Emotion Dataset (ASED). For
English, German and Urdu we use the existing RAVDESS, EMO-DB and URDU datasets.
We followed previous research in mapping labels for all datasets to just two
classes, positive and negative. Thus we can compare performance on different
languages directly, and combine languages for training and testing. In
Experiment 1, monolingual SER trials were carried out using three classifiers,
AlexNet, VGGE (a proposed variant of VGG), and ResNet50. Results averaged for
the three models were very similar for ASED and RAVDESS, suggesting that
Amharic and English SER are equally difficult. Similarly, German SER is more
difficult, and Urdu SER is easier. In Experiment 2, we trained on one language
and tested on another, in both directions for each pair: AmharicGerman,
AmharicEnglish, and AmharicUrdu. Results with Amharic as target suggested
that using English or German as source will give the best result. In Experiment
3, we trained on several non-Amharic languages and then tested on Amharic. The
best accuracy obtained was several percent greater than the best accuracy in
Experiment 2, suggesting that a better result can be obtained when using two or
three non-Amharic languages for training than when using just one non-Amharic
language. Overall, the results suggest that cross-lingual and multilingual
training can be an effective strategy for training a SER classifier when
resources for a language are scarce.Comment: 16 pages, 9 tables, 5 figure
Transductive Learning with String Kernels for Cross-Domain Text Classification
For many text classification tasks, there is a major problem posed by the
lack of labeled data in a target domain. Although classifiers for a target
domain can be trained on labeled text data from a related source domain, the
accuracy of such classifiers is usually lower in the cross-domain setting.
Recently, string kernels have obtained state-of-the-art results in various text
classification tasks such as native language identification or automatic essay
scoring. Moreover, classifiers based on string kernels have been found to be
robust to the distribution gap between different domains. In this paper, we
formally describe an algorithm composed of two simple yet effective
transductive learning approaches to further improve the results of string
kernels in cross-domain settings. By adapting string kernels to the test set
without using the ground-truth test labels, we report significantly better
accuracy rates in cross-domain English polarity classification.Comment: Accepted at ICONIP 2018. arXiv admin note: substantial text overlap
with arXiv:1808.0840
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