Ireland

Named entity recognition (NER) is one of the core technologies for knowledge acquisition from text and has been used for knowledge extraction of chemicals and medicine. 
As one of the NER improvement approaches, multitask learning that learns a model from multiple training data has been used. Among multitask learning, an auxiliary learning method, which uses training data of an auxiliary task for improving its target task, has shown higher NER performance than conventional multitask learning for improving all the tasks simultaneously. The conventional auxiliary learning method uses only one auxiliary training dataset. 
We propose Multiple Utilization of NER Corpora Helpful for Auxiliary BLESsing
(MUNCHABLES). MUNCHABLES utilizes multiple training datasets as auxiliary training data by the following methods : the first one is to fine-tune the NER model of the target task by sequentially performing auxiliary learning for each auxiliary training dataset, and the other is to use all training datasets in one auxiliary learning. We evaluate MUNCHABLES on eight chemical/biomedical/scientific domain NER tasks, where seven training datasets are used as auxiliary training data. The experiment results show that our proposed methods achieve higher NER performance than conventional multi-task learning methods on average and that NER performance can be improved by using multiple auxiliary training data. Furthermore, the proposed models outperform stateof-the-art models on the datasets

ACL 2022

Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data

named entity recognition

multi-task learning

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

workshop paper

Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these pre-training methods for genome understanding. However, they focus only on understanding haploid sequences, which hinders their applicability towards understanding genetic variations, also known as single nucleotide polymorphisms (SNPs), which is crucial for genome-wide association study. In this paper, we introduce SNP2Vec, a scalable self-supervised pre-training approach for understanding SNP. We apply SNP2Vec to perform long-sequence genomics modeling, and we evaluate the effectiveness of our approach on predicting Alzheimer's disease risk in a Chinese cohort. Our approach significantly outperforms existing polygenic risk score methods and all other baselines, including the model that is trained entirely with haploid sequences. We release our code and dataset on https://github.com/HLTCHKUST/snp2vec.

SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study

Automatic speech recognition (ASR) systems
usually incorporate postprocessing mechanisms
to remove disfluencies, facilitating the
generation of clear, fluent transcripts that are
conducive to many downstream NLP tasks.
However, verbal disfluencies have proved to
be predictive of dementia status, although little
is known about how various types of verbal
disfluencies, nor automatically detected disfluencies,
affect predictive performance. We experiment
with an off-the-shelf disfluency annotator
to tag disfluencies in speech transcripts
for a well-known cognitive health assessment
task. We evaluate the performance of this
model on detecting repetitions and corrections
or retracing, and measure the influence of goldannotated
versus automatically detected verbal
disfluencies on dementia detection through a
series of experiments. We find that removing
both gold and automatically-detected disfluencies
negatively impacts dementia detection
performance, degrading classification accuracy
by 5:6% and 3% respectively

How You Say It Matters: Measuring the Impact of Verbal Disfluency Tags on Automated Dementia Detection

We introduce Doctor XAvIer —a BERT-based diagnostic system that extracts relevant clinical data from transcribed patient-doctor dialogues and explains predictions using feature attribution methods. We present a novel performance plot and evaluation metric for feature attribution methods —Feature Attribution Dropping (FAD) curve and its Normalized Area Under the Curve (N-AUC). FAD curve analysis shows that integrated gradients outperforms Shapley values in explaining diagnosis classification. Doctor XAvIer outperforms the baseline with 0.97 F1-score in named entity recognition and symptom pertinence classification and 0.91 F1-score in diagnosis classification.

Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation

The PICO framework focuses on the Population, Intervention, Comparator, and Outcomes of clinical trial studies. Manually looking for PICO descriptions from the thousands of clinical studies is vital but one of the most resource-consuming tasks for writing systematic reviews. One of the significant barriers to fully automating PICO recognition is the lack of large manually annotated corpora. Distant supervision is one of the weak supervision methods used to obtain large entity annotated corpora. We propose a distant supervision method using open-access and open-source resources and use it to get a sizeable "Intervention" and "Comparator" entity annotated dataset called DISTANT-CTO. We use DISTANT-CTO to train downstream NER models and cross the previous state of the art on "Intervention" and "Comparator" extraction.

DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature

Recognition of named entities present in text is an important step towards information extraction and natural language understanding. This work presents a named entity recognition system for the Romanian biomedical domain. The system makes use of a new and extended version of SiMoNERo corpus, that is open sourced. Additionally, the best system is available for direct usage in the RELATE platform.

Improving Romanian BioNER Using a Biologically Inspired System

This work presents the first large-scale biomedical Spanish language models trained from scratch, using large biomedical corpora consisting of a total of 1.1B tokens and an EHR corpus of 95M tokens. We compared them against general-domain and other domain-specific models for Spanish on three clinical NER tasks. As main results, our models are superior across the NER tasks, rendering them more convenient for clinical NLP applications. Furthermore, our findings indicate that when enough data is available, pre-training from scratch is better than continual pre-training when tested on clinical tasks, raising an exciting research question about which approach is optimal. Our models and fine-tuning scripts are publicly available at HuggingFace and GitHub.

Pretrained Biomedical Language Models for Clinical NLP in Spanish

Biomedical Named Entity Recognition (BM-NER) is one of the most important tasks in the field of biomedical text mining. Most work so far on this task has not focused on identification of discontinuous and overlapping entities, even though they are present in significant fractions in real-life biomedical datasets.

In this paper, we introduce a novel annotation schema to capture complex entities, and explore the effects of distant supervision on our deep-learning sequence labelling model. For BMNER task, our annotation schema outperforms other BIO-based annotation schemes on the same model. We also achieve higher F1-scores than state-of-the-art models on multiple corpora without fine-tuning embeddings, highlighting the efficacy of neural feature extraction using our model.

Biomedical NER using Novel Schema and Distant Supervision

Research papers reflect scientific advances. Citations are widely used in research publications to support the new findings and show their benefits, while also regulating the information flow to make the contents clearer for the audience. A citation in a research article refers to the information's source, but not the specific text span from that source article. In biomedical research articles, this task is challenging as the same chemical or biological component can be represented in multiple ways in different papers from various domains. This paper suggests a mechanism for linking citing sentences in a publication with cited sentences in referenced sources. The framework presented here pairs the citing sentence with all of the sentences in the reference text, and then tries to retrieve the semantically equivalent pairs. These semantically related sentences from the reference paper are chosen as the cited statements. This effort involves designing a citation linkage framework utilizing sequential and tree-structured siamese deep learning models. This paper also provides a method to create an automatically generated corpus for such a task.

BioCite: A Deep Learning-based Citation Linkage Framework for Biomedical Research Articles

Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length. In this work, we focus on echocardiogram notes that are longer and more complex compared to previous note types. We formally define the task of echocardiography conclusion generation (EchoGen) as generating a conclusion given the findings section, with emphasis on key cardiac findings. To promote the development of EchoGen methods, we present a new benchmark, which consists of two datasets collected from two hospitals. We further compare both standard and state-of-the-art methods on this new benchmark, with an emphasis on factual consistency. To accomplish this, we develop a tool to automatically extract concept-attribute tuples from the text. We then propose an evaluation metric, FactComp, to compare concept-attribute tuples between the human reference and generated conclusions. Both automatic and human evaluations show that there is still a significant gap between human-written and machine-generated conclusions on echo reports in terms of factuality and overall quality.

EchoGen: Generating Conclusions from Echocardiogram Notes

A wealth of important clinical information lies untouched in the Electronic Health Record, often in the form of unstructured textual documents. For patients with Epilepsy, such information includes outcome measures like Seizure Frequency and Dates of Last Seizure, key parameters that guide all therapy for these patients. Transformer models have been able to extract such outcome measures from unstructured clinical note text as sentences with human-like accuracy; however, these sentences are not yet usable in a quantitative analysis for large-scale studies. In this study, we developed a pipeline to quantify these outcome measures. We used text summarization models to convert unstructured sentences into specific formats, and then employed rules-based quantifiers to calculate seizure frequencies and dates of last seizure. We demonstrated that our pipeline of models does not excessively propagate errors and we analyzed its mistakes. We anticipate that our methods can be generalized outside of epilepsy to other disorders to drive large-scale clinical research.

Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data

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