Malta

Since medical text cannot be shared easily due to privacy concerns, synthetic data bears much potential for natural language processing applications. In the context of social media and user-generated messages about drug intake and adverse drug effects, this work presents different methods to examine the authenticity of synthetic text. We conclude that the generated tweets are untraceable and show enough authenticity from the medical point of view to be used as a replacement for a real Twitter corpus. However, original data might still be the preferred choice as they contain much more diversity.

EACL 2024

Assessing authenticity and anonymity of synthetic user-generated content in the medical domain

user-generated; adverse drug reactions; synthetic data

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

workshop paper

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

In this paper we evaluate two annotation approaches for automatic detection and labelling of personal information in legal texts in relation to the ambiguity of the labels and the homogeneity of the annotations. For this purpose, we built a corpus of 44 case reports from the European Court of Human Rights in Spanish language and we annotated it following two different annotation approaches: automatic projection of the annotations of an existing English corpus, and manual annotation with our reinterpretation of their guidelines. Moreover, we employ Flair on a Named Entity Recognition task to compare its performance in the two annotation schemes.

Automatic Detection and Labelling of Personal Data in Case Reports from the ECHR in Spanish: Evaluation of Two Different Annotation Approaches

Since the announcement of the GDPR, the pseudonymization of legal documents has become a high-priority task in many legal organizations. This means that for making public a document, it is necessary to redact the identity of certain entities, such as witnesses. In this work, we present the first results obtained by PSILENCE, a pseudonymization tool created for redacting semi-automatically international arbitration documents in English. PSILENCE has been built using a Named Entity Recognition (NER) system, along with a Coreference Resolution system. These systems allow us to find the people that we need to redact in a clustered way, but also to propose the same pseudonym throughout one document. This last aspect makes it easier to read and comprehend a redacted legal document. Different experiments were done on four different datasets, one of which was legal, and the results are promising, reaching a Macro F-score of up to 0.72 on the legal dataset.

PSILENCE: A Pseudonymization Tool for International Law

The study discusses the methods and challenges of deidentifying and pseudonymizing Norwegian clinical text for research purposes. The results of the NorDeid tool for deidentification and pseudonymization on different types of protected health information were evaluated and discussed, as well as the extension of its functionality with regular expressions to identify specific types of sensitive information. The research used a clinical corpus of adult patients treated in a gastro-surgical department in Norway, which contains approximately nine million clinical notes. The study also highlights the challenges posed by the unique language and clinical terminology of Norway and emphasizes the importance of protecting privacy and the need for customized approaches to meet legal and research requirements.

Deidentifying a Norwegian clinical corpus - An effort to create a privacy-preserving Norwegian large clinical language model

We present the findings and results of our pseudonymisation system, which has been developed for a real-life use-case involving users and an informative chatbot in the context of the COVID-19 pandemic. Message exchanges between the two involve the former group providing information about themselves and their residential area, which could easily allow for their re-identification. We create a modular pipeline to detect PIIs and perform basic deidentification such that the data can be stored while mitigating any privacy concerns. The use-case presents several challenging aspects, the most difficult of which is the logistic challenge of not being able to directly view or access the data due to the very privacy issues we aim to resolve. Nevertheless, our system achieves a high recall of 0.99, correctly identifying almost all instances of personal data. However, this comes at the expense of precision, which only reaches 0.64. We describe the sensitive information identification in detail, explaining the design principles behind our decisions. We additionally highlight the particular challenges we've encountered.

Extending off-the-shelf NER Systems to Personal Information Detection in Dialogues with a Virtual Agent: Findings from a Real-Life Use Case

Linguistic data can — and often does — contain PII (Personal Identifiable Information). Both from a legal and ethical standpoint, the sharing of such data is not permissible. According to the GDPR, pseudonymization, i.e. the replacement of sensitive information with surrogates, is an acceptable strategy for privacy preservation. While research has been conducted on the detection and replacement of sensitive data in Swedish medical data using Large Language Models (LLMs), it is unclear whether these models handle PII in less structured and more thematically varied texts equally well. In this paper, we present and discuss the performance of an LLM-based PII-detection system for Swedish learner essays.

Detecting Personal Identifiable Information in Swedish Learner Essays

Large language models in public-facing industrial applications must accurately process data for the domain in which they are deployed, but they must not leak sensitive or confidential information when used. We present a process for anonymizing training data, a framework for quantitatively and qualitatively assessing the effectiveness of this process, and an assessment of the effectiveness of models fine-tuned on anonymized data in comparison with commercially available LLM APIs.

Data Anonymization for Privacy-Preserving Large Language Model Fine-Tuning on Call Transcripts

Clinical data, in the form of electronic health records, are rich resources that can be tapped using natural language processing. At the same time, they contain very sensitive information that must be protected. One strategy is to remove or obscure data using automatic de-identification. However, the detection of sensitive data can yield false positives. This is especially true for tokens that are similar in form to sensitive entities, such as eponyms. These names tend to refer to medical procedures or diagnoses rather than specific persons. Previous research has shown that automatic de-identification systems often misclassify eponyms as names, leading to a loss of valuable medical information. In this study, we estimate the prevalence of eponyms in a real Swedish clinical corpus. Furthermore, we demonstrate that modern transformer-based de-identification systems are more accurate in distinguishing between names and eponyms than previous approaches.

When Is a Name Sensitive? Eponyms in Clinical Text and Implications for De-Identification

Automated essay scoring of second-language learner essays is a high-stakes task, as it can affect the job and educational opportunities a student may have access to. Thus, it becomes imperative to make sure that the essays are graded based on the students' language proficiency as opposed other reasons, such as personal names used in the text of the essay. Moreover, most of the research data is not freely accessible, as it tends to contain personal identifiable information. Because of that, pseudonlymization becomes an important tool to make sure that this data can be freely shared. Thus, our systems should not grade students based on which first names were used in the text of the essay, both for fairness and for privacy reasons. In this paper we explore how first names affect the CEFR level classification of essays of second language learners of Swedish. We used essays containing just one personal name and substituted it for names from lists of first names from four different ethnic origins, namely Swedish, Finnish, Anglo-American, and Arabic. We found that changing the names within the essays had no apparent effect on the classification task, regardless of whether a feature-based or a transformer-based model was used.

Did the Names I Used within My Essay Affect My Score? Diagnosing Name Biases in Automated Essay Scoring

We describe the second-place submission for the shared task organized at the Fourth Workshop on Language Technology for Equality, Diversity, and Inclusion (LT-EDI-2024). The task focuses on detecting caste/immigration hate speech in Tamil. The included texts involve the Tamil language in both Tamil script and transliterated into Latin script, with some texts also in English. Considering different scripts, we examined the performance of 12 transformer language models on the dev set. Our analysis revealed that for the whole dataset, the model google/muril-large-cased performs the best. We used an ensemble of several models for the final challenge submission, achieving 0.81 for the test dataset.

kubapok@LT-EDI 2024: Evaluating Transformer Models for Hate Speech Detection in Tamil

This paper describes our homophobia/transphobia in social media comments detection system developed as part of the shared task at LT-EDI-2024. We took a transformer-based approach to develop our multiclass classification model for ten language conditions (English, Spanish, Gujarati, Hindi, Kannada, Malayalam, Marathi, Tamil, Tulu, and Telugu). We introduced synthetic and organic instances of script-switched language data during domain adaptation to mirror the linguistic realities of social media language as seen in the labelled training data. Our system ranked second for Gujarati and Telugu with varying levels of performance for other language conditions. The results suggest incorporating elements of paralinguistic behaviour such as script-switching may improve the performance of language detection systems especially in the cases of under-resourced languages conditions.

Downloads

Next from EACL 2024

Automatic Detection and Labelling of Personal Data in Case Reports from the ECHR in Spanish: Evaluation of Two Different Annotation Approaches

Similar lecture

Evaluating Synthetic Data Generation from User Generated Text

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from EACL 2024

Automatic Detection and Labelling of Personal Data in Case Reports from the ECHR in Spanish: Evaluation of Two Different Annotation Approaches

Similar lecture

Evaluating Synthetic Data Generation from User Generated Text

Downloads