61,152 research outputs found
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media
Sentiment analysis has been emerging recently as one of the major natural
language processing (NLP) tasks in many applications. Especially, as social
media channels (e.g. social networks or forums) have become significant sources
for brands to observe user opinions about their products, this task is thus
increasingly crucial. However, when applied with real data obtained from social
media, we notice that there is a high volume of short and informal messages
posted by users on those channels. This kind of data makes the existing works
suffer from many difficulties to handle, especially ones using deep learning
approaches. In this paper, we propose an approach to handle this problem. This
work is extended from our previous work, in which we proposed to combine the
typical deep learning technique of Convolutional Neural Networks with domain
knowledge. The combination is used for acquiring additional training data
augmentation and a more reasonable loss function. In this work, we further
improve our architecture by various substantial enhancements, including
negation-based data augmentation, transfer learning for word embeddings, the
combination of word-level embeddings and character-level embeddings, and using
multitask learning technique for attaching domain knowledge rules in the
learning process. Those enhancements, specifically aiming to handle short and
informal messages, help us to enjoy significant improvement in performance once
experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in
IJCVR on September 201
Automatically detecting open academic review praise and criticism
This is an accepted manuscript of an article published by Emerald in Online Information Review on 15 June 2020.
The accepted version of the publication may differ from the final published version, accessible at https://doi.org/10.1108/OIR-11-2019-0347.Purpose: Peer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research’s open peer review publishing platform.
Design/methodology/approach: PeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings.
Findings: PeerJudge can predict F1000Research judgements from negative evaluations in reviewers’ comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision.
Originality/value: PeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments
Learning and Interpreting Multi-Multi-Instance Learning Networks
We introduce an extension of the multi-instance learning problem where
examples are organized as nested bags of instances (e.g., a document could be
represented as a bag of sentences, which in turn are bags of words). This
framework can be useful in various scenarios, such as text and image
classification, but also supervised learning over graphs. As a further
advantage, multi-multi instance learning enables a particular way of
interpreting predictions and the decision function. Our approach is based on a
special neural network layer, called bag-layer, whose units aggregate bags of
inputs of arbitrary size. We prove theoretically that the associated class of
functions contains all Boolean functions over sets of sets of instances and we
provide empirical evidence that functions of this kind can be actually learned
on semi-synthetic datasets. We finally present experiments on text
classification, on citation graphs, and social graph data, which show that our
model obtains competitive results with respect to accuracy when compared to
other approaches such as convolutional networks on graphs, while at the same
time it supports a general approach to interpret the learnt model, as well as
explain individual predictions.Comment: JML
General Purpose Textual Sentiment Analysis and Emotion Detection Tools
Textual sentiment analysis and emotion detection consists in retrieving the
sentiment or emotion carried by a text or document. This task can be useful in
many domains: opinion mining, prediction, feedbacks, etc. However, building a
general purpose tool for doing sentiment analysis and emotion detection raises
a number of issues, theoretical issues like the dependence to the domain or to
the language but also pratical issues like the emotion representation for
interoperability. In this paper we present our sentiment/emotion analysis
tools, the way we propose to circumvent the di culties and the applications
they are used for.Comment: Workshop on Emotion and Computing (2013
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
As of February 2016 Facebook allows users to express their experienced
emotions about a post by using five so-called `reactions'. This research paper
proposes and evaluates alternative methods for predicting these reactions to
user posts on public pages of firms/companies (like supermarket chains). For
this purpose, we collected posts (and their reactions) from Facebook pages of
large supermarket chains and constructed a dataset which is available for other
researches. In order to predict the distribution of reactions of a new post,
neural network architectures (convolutional and recurrent neural networks) were
tested using pretrained word embeddings. Results of the neural networks were
improved by introducing a bootstrapping approach for sentiment and emotion
mining on the comments for each post. The final model (a combination of neural
network and a baseline emotion miner) is able to predict the reaction
distribution on Facebook posts with a mean squared error (or misclassification
rate) of 0.135.Comment: 10 pages, 13 figures and accepted at ICAART 2018. (Dataset:
https://github.com/jerryspan/FacebookR
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