Korea, Republic of

In the news, statements from information sources are often quoted, made by individuals who interact in the news. Detecting those quotes and the gender of their sources is a key task when it comes to media analysis from a gender perspective. It is a challenging task: the structure of the quotes is variable, gender marks are not present in many languages, and quote authors are often omitted due to frequent use of coreferences. This paper proposes a strategy to measure the presence of women and men as information sources in news. We approach the problem of detecting sentences including quotes and the gender of the speaker as a joint task, by means of a supervised multiclass classifier of sentences. We have created the first datasets for Spanish and Basque by manually annotating quotes and the gender of the associated sources in news items. The results obtained show that BERT based approaches are significantly better than bag-of-words based classical ones, achieving accuracies close to 90%. We also analyse a bilingual learning strategy and generating additional training examples synthetically; both provide improvements up to 3.4% and 5.6\%, respectively.

COLING 2022

 Measuring Presence of Women and Men as Information Sources in News

quote author classification

quote identification

gender bias

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

This paper presents a description of our system in SMM4H-2022, where we participated in task 1a, task 4, and task 6 to task 10. There are three main challenges in SMM4H-2022, namely the domain shift problem, the prediction bias due to category imbalance, and the noise in infor mal text. In this paper, we propose a unified framework for the classification and named en- tity recognition tasks to solve the challenges, and it can be applied to both English and Spanish scenarios. The results of our system are higher than the median F1-scores for 7 tasks and significantly exceed the F1-scores for 5 tasks. The experimental results demonstrate the effectiveness of our system.

A Uniform Health Information Mining System for Multilingual Social Media Texts

We participated in the Social Media Mining for Health 2022 Shared Task with two separate systems for detecting adverse drug events (ADEs) in English tweets (Task 1a) and extracting ADE spans in such tweets (Task 1b). The systems rely on the T5 model and its sequence-to-sequence formulation. To address the class imbalance, we made use of data balancing via over- and undersampling on both tasks. For the ADE extraction task, we explored prompting to further benefit from the T5 model and its formulation. Additionally, we built an ensemble model, utilizing both balanced and prompted models. The proposed models outperformed the current state-of-the-art, with an F1 score of 0.655 on ADE classification and a Partial F1 score of 0.527 on ADE extraction.

Text-to-Text Adverse Drug Event Extractionwith Data Balancing and Prompting

SMM4H-2022~\cite{overviewsmm4h} Task 2 is to detect whether containing premise in the tweets of users about COVID-19 on the social medias or their stances for the claims. In this paper, we propose \textbf{T}weet \textbf{C}laim \textbf{M}atching (\textbf{TCM}), which is a new pre-training task constructed by the tweets and claims similarly to Next Sentence Prediction (NSP). We first continue to pre-train the standard pre-trained language models on the labelled dataset and then fine-tune them for obtaining better performance. Compared with the solid baseline~\cite{glandt2021stance}, we achieve the absolute improvement of 7.9\% in Task 2a and obtain the SOTA results.

The Pre-training Tweet \& Claim Matching Makes Your Prediction Better

The reported work is our straightforward approach for the shared task “Classification of tweets self-reporting age” organized by the
“Social Media Mining for Health Applications (SMM4H)” workshop. This poster describes the approach that was used to build a binary classification system, that classifies the tweets related to birthday posts into two classes namely, exact age(positive class) and non-exactage(negative class).

BERT for Classification of Twitter Posts

We describe the work of the READ-BioMed team for the preparation of a submission to the SocialDisNER Disease Named Entity Recognition (NER) Task (Task 10) in 2022. We had developed a system for named entity recognition for identifying biomedical concepts in English MEDLINE citations and Spanish clinical text for the LivingNER 2022 challenge. Minimal adaptation of our system was required to perform named entity recognition in the Spanish tweets in the SocialDisNER task, given the availability of Spanish pre-trained language models and the SocialDisNER training data. Minor additions included treatment of emojis and entities in hashtags and Twitter account names.

Adaptation of an Annotation System to Spanish Tweets

In this presentation, we describe our approaches to solving the SocialDisNER task, which belongs to the Social Media Mining for Health Applications (SMM4H) shared task. This task aims to identify disease mentions in tweets written in Spanish. The proposed model is an architecture based on the FLERT approach. It consists of fine-tuning a language model that creates an input representation of a sentence based on its neighboring sentences, thus obtaining the document-level context. The best result was obtained using an ensemble of six language models using the FLERT approach. The system achieved an F1 score of 0.862, significantly surpassing the average performance among competitor models of 0.680 on the test partition.

Improving Detection of Disease Mentions in Tweets by Using Document-Level Features

This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which concerns the recognition of diseases mentioned in Spanish tweets. Before classifying each token, we encode each token with a transformer encoder using features from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMIST gazetteer, among others. We obtain a strict F1 score of 0.869, with competition mean of 0.675, standard deviation of 0.245, and median of 0.761.

Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets

This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2022 shared tasks. We participated in 2 tasks: a) Task 4: Classification of Tweets self-reporting exact age and b) Task 9: Classification of Reddit posts self-reporting exact age. We evaluated the two( BERT and RoBERTa) transformer-based models for both tasks. For Task 4 RoBERTa-Large achieved an F1 score of 0.846 on the test set and BERT-Large achieved an F1 score of 0.865 on the test set for Task 9.

A BERT-based approach for classification ofsocial media forum posts

My presentation describes BioInfo@UAVR team’s approach for adressing subtasks 1a and 1b of the Social Media Mining for Health Applications 2022 shared task. These sub-tasks deal with the classification of tweets that contain an Adverse Drug Event mentions and the detection of spans that correspond to those mentions. Our approach relies on transformer-based models, data augmentation, and an external dataset.

Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models

ITAINNOVA participates in SocialDisNER with a hybrid system which combines Transformer-based Language Models (LMs) with a custom-built gazetteer for Approximate String Matching (ASM) and dedicated text processing techniques for the social media domain. Additionally, zero-shot classification capabilities have been explored in order to support different parts of the system. An extensive analysis on the interactions of these components has been accomplished, making the system stand out above the mean performance of all the participating teams.

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