266 research outputs found
Combining Textual Features for the Detection of Hateful and Offensive Language
The detection of offensive, hateful and profane language has become a critical challenge since many users in social networks are exposed to cyberbullying activities on a daily basis. In this paper, we present an analysis of combining different textual features for the detection of hateful or offensive posts on Twitter. We provide a detailed experimental evaluation to understand the impact of each building block in a neural network architecture. The proposed architecture is evaluated on the English Subtask 1A: Identifying Hate, offensive and profane content from the post datasets of HASOC-2021 dataset under the team name TIB-VA. We compared different variants of the contextual word embeddings combined with the character level embeddings and the encoding of collected hate terms
IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
[EN] This overview paper describes the first shared task on irony
detection for the Arabic language. The task consists of a binary classification of tweets as ironic or not using a dataset composed of 5,030
Arabic tweets about different political issues and events related to the
Middle East and the Maghreb. Tweets in our dataset are written in
Modern Standard Arabic but also in different Arabic language varieties
including Egypt, Gulf, Levantine and Maghrebi dialects. Eighteen teams
registered to the task among which ten submitted their runs. The methods of participants ranged from feature-based to neural networks using
either classical machine learning techniques or ensemble methods. The
best performing system achieved F-score value of 0.844, showing that
classical feature-based models outperform the neural ones.This publication was made possible by NPRP grant 9-175-1-033 from the Qatar
National Research Fund (a member of Qatar Foundation). The findings achieved
herein are solely the responsibility of the last author. The work of Paolo Rosso
was also partially funded by Generalitat Valenciana under grant PROMETEO/2019/121.Ghanem, B.; Karoui, J.; Benamara, F.; Moriceau, V.; Rosso, P. (2019). IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets. CEUR-WS.org. 380-390. http://hdl.handle.net/10251/180744S38039
Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021
Automatic detection of fake news is a highly important task in the
contemporary world. This study reports the 2nd shared task called
UrduFake@FIRE2021 on identifying fake news detection in Urdu. The goal of the
shared task is to motivate the community to come up with efficient methods for
solving this vital problem, particularly for the Urdu language. The task is
posed as a binary classification problem to label a given news article as a
real or a fake news article. The organizers provide a dataset comprising news
in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and
(v) Business, split into training and testing sets. The training set contains
1300 annotated news articles -- 750 real news, 550 fake news, while the testing
set contains 300 news articles -- 200 real, 100 fake news. 34 teams from 7
different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE)
registered to participate in the UrduFake@FIRE2021 shared task. Out of those,
18 teams submitted their experimental results, and 11 of those submitted their
technical reports, which is substantially higher compared to the UrduFake
shared task in 2020 when only 6 teams submitted their technical reports. The
technical reports submitted by the participants demonstrated different data
representation techniques ranging from count-based BoW features to word vector
embeddings as well as the use of numerous machine learning algorithms ranging
from traditional SVM to various neural network architectures including
Transformers such as BERT and RoBERTa. In this year's competition, the best
performing system obtained an F1-macro score of 0.679, which is lower than the
past year's best result of 0.907 F1-macro. Admittedly, while training sets from
the past and the current years overlap to a large extent, the testing set
provided this year is completely different
Detecting offensive speech in conversational code-mixed dialogue on social media: A contextual dataset and benchmark experiments
The spread of Hate Speech on online platforms is a severe issue for societies and requires the identification of offensive content by platforms. Research has modeled Hate Speech recognition as a text classification problem that predicts the class of a message based on the text of the message only. However, context plays a huge role in communication. In particular, for short messages, the text of the preceding tweets can completely change the interpretation of a message within a discourse. This work extends previous efforts to classify Hate Speech by considering the current and previous tweets jointly. In particular, we introduce a clearly defined way of extracting context. We present the development of the first dataset for conversational-based Hate Speech classification with an approach for collecting context from long conversations for code-mixed Hindi (ICHCL dataset). Overall, our benchmark experiments show that the inclusion of context can improve classification performance over a baseline. Furthermore, we develop a novel processing pipeline for processing the context. The best-performing pipeline uses a fine-tuned SentBERT paired with an LSTM as a classifier. This pipeline achieves a macro F1 score of 0.892 on the ICHCL test dataset. Another KNN, SentBERT, and ABC weighting-based pipeline yields an F1 Macro of 0.807, which gives the best results among traditional classifiers. So even a KNN model gives better results with an optimized BERT than a vanilla BERT model
Cross-language Information Retrieval
Two key assumptions shape the usual view of ranked retrieval: (1) that the
searcher can choose words for their query that might appear in the documents
that they wish to see, and (2) that ranking retrieved documents will suffice
because the searcher will be able to recognize those which they wished to find.
When the documents to be searched are in a language not known by the searcher,
neither assumption is true. In such cases, Cross-Language Information Retrieval
(CLIR) is needed. This chapter reviews the state of the art for CLIR and
outlines some open research questions.Comment: 49 pages, 0 figure
Optimization of RBF-SVM hyperparameters using genetic algorithm for face recognit
Manual grid-search tuning of machine learning hyperparameters is very time-consuming. Hence, to curb this problem, we propose the use of a genetic algorithm (GA) for the selection of optimal radial-basis-function based support vector machine (RBF-SVM) hyperparameters; regularization parameter C and cost-factor γ. The resulting optimal parameters were used during the training of face recognition models. To train the models, we independently extracted features from the ORL face image dataset using local binary patterns (handcrafted) and deep learning architectures (pretrained variants of VGGNet). The resulting features were passed as input to either linear-SVM or optimized RBF-SVM. The results show that the models from optimized RBFSVM combined with deep learning or hand-crafted features yielded performances that surpass models obtained from Linear-SVM combined with the aforementioned features in most of the data splits. The study demonstrated that it is profitable to optimize the hyperparameters of an SVM to obtain the best classification performance.
Keywords: Face Recognition, Feature Extraction, Local Binary Patterns, Transfer Learning, Genetic Algorithm and Support Vector Machines
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