pwc-archive/papers-with-abstracts huggingface dataset:
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models
Authors: Mohammad Golam Sohrab, Minh Thang Pham, Makoto Miwa, Hiroya Takamura
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Entity Embeddings, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Task 2

We present a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI), which links them to concept unique identifiers (CUIs) in a knowledge base, for the PharmaCoNER shared task on pharmaceutical drugs and chemical entities. We proposed a neural NER model that captures the surrounding semantic information of a given sequence by capturing the forward- and backward-context of bidirectional LSTM (Bi-LSTM) output of a target span using contextual span representation-based exhaustive approach. The NER model enumerates all possible spans as potential entity mentions and classify them into entity types or no entity with deep neural networks. For representing span, we compare several different neural network architectures and their ensembling for the NER model. We then perform dictionary matching for CI and, if there is no matching, we further compute similarity scores between a mention and CUIs using entity embeddings to assign the CUI with the highest score to the mention. We evaluate our approach on the two sub-tasks in the shared task. Among the five submitted runs, the best run for each sub-task achieved the F-score of 86.76{\%} on Sub-task 1 (NER) and the F-score of 79.97{\%} (strict) on Sub-task 2 (CI).
Biomedical Named Entity Recognition with Multilingual BERT
Authors: Kai Hakala, Sampo Pyysalo
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

We present the approach of the Turku NLP group to the PharmaCoNER task on Spanish biomedical named entity recognition. We apply a CRF-based baseline approach and multilingual BERT to the task, achieving an F-score of 88{\%} on the development data and 87{\%} on the test set with BERT. Our approach reflects a straightforward application of a state-of-the-art multilingual model that is not specifically tailored to either the language nor the application domain. The source code is available at: https://github.com/chaanim/pharmaconer
An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks
Authors: Yuxing Wang, Kaiyin Zhou, Mina Gachloo, Jingbo Xia
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation Extraction, Topic Classification

The active gene annotation corpus (AGAC) was developed to support knowledge discovery for drug repurposing. Based on the corpus, the AGAC track of the BioNLP Open Shared Tasks 2019 was organized, to facilitate cross-disciplinary collaboration across BioNLP and Pharmacoinformatics communities, for drug repurposing. The AGAC track consists of three subtasks: 1) named entity recognition, 2) thematic relation extraction, and 3) loss of function (LOF) / gain of function (GOF) topic classification. The AGAC track was participated by five teams, of which the performance are compared and analyzed. The the results revealed a substantial room for improvement in the design of the task, which we analyzed in terms of {``}imbalanced data{''}, {``}selective annotation{''} and {``}latent topic annotation{''}.
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
Authors: Dongfang Li, Ying Xiong, Baotian Hu, Hanyang Du, Buzhou Tang, Qingcai Chen
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Drug Discovery, Multi-Task Learning, NER, Relation Extraction, Task 2

The prediction of the relationship between the disease with genes and its mutations is a very important knowledge extraction task that can potentially help drug discovery. In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019. Task 1 can be regarded as the traditional name entity recognition (NER), which cultivates molecular phenomena related to gene mutation. Task 2 can be regarded as relation extraction which captures the thematic roles between entities. For two tasks, we exploit the pre-trained biomedical language representation model (i.e., BERT) in the pipe of information extraction for the collection of mutation-disease knowledge from PubMed. And also, we design a fine-tuning technique and extra features by using multi-task learning. The experiment results show that our proposed approaches achieve 0.60 (ranks 1) and 0.25 (ranks 2) on task 1 and task 2 respectively in terms of $F_1$ metric.
DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature
Authors: Feifan Liu, Xiaoyu Zheng, Bo wang, Catarina Kiefe
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Drug Discovery, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Relation Extraction

Understanding the pathogenesis of genetic diseases through different gene activities and their relations to relevant diseases is important for new drug discovery and drug repositioning. In this paper, we present a joint deep learning model in a multi-task learning paradigm for gene mutation-disease knowledge extraction, DeepGeneMD, which adapts the state-of-the-art hierarchical multi-task learning framework for joint inference on named entity recognition (NER) and relation extraction (RE) in the context of the AGAC (Active Gene Annotation Corpus) track at 2019 BioNLP Open Shared Tasks (BioNLP-OST). It simultaneously extracts gene mutation related activities, diseases, and their relations from the published scientific literature. In DeepGeneMD, we explore the task decomposition to create auxiliary subtasks so that more interactions between different learning subtasks can be leveraged in model training. Our model achieves the average F1 score of 0.45 on recognizing gene activities and disease entities, ranking 2nd in the AGAC NER task; and the average F1 score of 0.35 on extracting relations, ranking 1st in the AGAC RE task.
RACAI's System at PharmaCoNER 2019
Authors: Radu Ion, Vasile Florian P{\u{a}}i{\textcommabelow{s}}, Maria Mitrofan
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, POS

This paper describes the Named Entity Recognition system of the Institute for Artificial Intelligence {``}Mihai Dr{\u{a}}g{\u{a}}nescu{''} of the Romanian Academy (RACAI for short). Our best F1 score of 0.84984 was achieved using an ensemble of two systems: a gazetteer-based baseline and a RNN-based NER system, developed specially for PharmaCoNER 2019. We will describe the individual systems and the ensemble algorithm, compare the final system to the current state of the art, as well as discuss our results with respect to the quality of the training data and its annotation strategy. The resulting NER system is language independent, provided that language-dependent resources and preprocessing tools exist, such as tokenizers and POS taggers.
Transfer Learning in Biomedical Named Entity Recognition: An Evaluation of BERT in the PharmaCoNER task
Authors: Cong Sun, Zhihao Yang
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

To date, a large amount of biomedical content has been published in non-English texts, especially for clinical documents. Therefore, it is of considerable significance to conduct Natural Language Processing (NLP) research in non-English literature. PharmaCoNER is the first Named Entity Recognition (NER) task to recognize chemical and protein entities from Spanish biomedical texts. Since there have been abundant resources in the NLP field, how to exploit these existing resources to a new task to obtain competitive performance is a meaningful study. Inspired by the success of transfer learning with language models, we introduce the BERT benchmark to facilitate the research of PharmaCoNER task. In this paper, we evaluate two baselines based on Multilingual BERT and BioBERT on the PharmaCoNER corpus. Experimental results show that transferring the knowledge learned from source large-scale datasets to the target domain offers an effective solution for the PharmaCoNER task.
A Multi-Task Learning Framework for Extracting Bacteria Biotope Information
Authors: Qi Zhang, Chao Liu, Ying Chi, Xuansong Xie, Xian-Sheng Hua
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Multi-Task Learning, NER, Relation Extraction, Sentence

This paper presents a novel transfer multi-task learning method for Bacteria Biotope rel+ner task at BioNLP-OST 2019. To alleviate the data deficiency problem in domain-specific information extraction, we use BERT(Bidirectional Encoder Representations from Transformers) and pre-train it using mask language models and next sentence prediction on both general corpus and medical corpus like PubMed. In fine-tuning stage, we fine-tune the relation extraction layer and mention recognition layer designed by us on the top of BERT to extract mentions and relations simultaneously. The evaluation results show that our method achieves the best performance on all metrics (including slot error rate, precision and recall) in the Bacteria Biotope rel+ner subtask.
YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction
Authors: Junyi Li, Xiaobing Zhou, Yuhang Wu, Bin Wang
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Binary Relation Extraction, Event Extraction, Relation, Relation Extraction

We participated in the BioNLP 2019 Open Shared Tasks: binary relation extraction of SeeDev task. The model was constructed us- ing convolutional neural networks (CNN) and long short term memory networks (LSTM). The full text information and context information were collected using the advantages of CNN and LSTM. The model consisted of two main modules: distributed semantic representation construction, such as word embedding, distance embedding and entity type embed- ding; and CNN-LSTM model. The F1 value of our participated task on the test data set of all types was 0.342. We achieved the second highest in the task. The results showed that our proposed method performed effectively in the binary relation extraction.
Using Snomed to recognize and index chemical and drug mentions.
Authors: Pilar L{\'o}pez {\'U}beda, Manuel Carlos D{\'\i}az Galiano, L. Alfonso Urena Lopez, Maite Martin
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: BIG-bench Machine Learning, Entity Extraction using GAN

In this paper we describe a new named entity extraction system. Our work proposes a system for the identification and annotation of drug names in Spanish biomedical texts based on machine learning and deep learning models. Subsequently, a standardized code using Snomed is assigned to these drugs, for this purpose, Natural Language Processing tools and techniques have been used, and a dictionary of different sources of information has been built. The results are promising, we obtain 78{\%} in F1 score on the first sub-track and in the second task we map with Snomed correctly 72{\%} of the found entities.
Bacteria Biotope at BioNLP Open Shared Tasks 2019
Authors: Robert Bossy, Louise Del{\'e}ger, Estelle Chaix, Mouhamadou Ba, Claire N{\'e}dellec
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11

This paper presents the fourth edition of the Bacteria Biotope task at BioNLP Open Shared Tasks 2019. The task focuses on the extraction of the locations and phenotypes of microorganisms from PubMed abstracts and full-text excerpts, and the characterization of these entities with respect to reference knowledge sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by the importance of the knowledge on biodiversity for fundamental research and applications in microbiology. The paper describes the different proposed subtasks, the corpus characteristics, and the challenge organization. We also provide an analysis of the results obtained by participants, and inspect the evolution of the results since the last edition in 2016.
An ensemble CNN method for biomedical entity normalization
Authors: Pan Deng, Haipeng Chen, Mengyao Huang, Xiaowen Ruan, Liang Xu
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Entity Linking, Medical Concept Normalization

Different representations of the same concept could often be seen in scientific reports and publications. Entity normalization (or entity linking) is the task to match the different representations to their standard concepts. In this paper, we present a two-step ensemble CNN method that normalizes microbiology-related entities in free text to concepts in standard dictionaries. The method is capable of linking entities when only a small microbiology-related biomedical corpus is available for training, and achieved reasonable performance in the online test of the BioNLP-OST19 shared task Bacteria Biotope.
BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes
Authors: {\.I}lknur Karadeniz, {\"O}mer Faruk Tuna, Arzucan {\"O}zg{\"u}r
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Medical Concept Normalization, Relation Extraction, Re-Ranking, Word Embeddings

This paper presents our participation to the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel). For the normalization of entities, we utilized word embeddings and syntactic re-ranking. For the relation extraction task, pre-defined rules are used. Although both approaches are unsupervised, in the sense that they do not need any labeled data, they achieved promising results. Especially, for the BB-norm task, the results have shown that the proposed method performs as good as deep learning based methods, which require labeled data.
Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs
Authors: Wuti Xiong, Fei Li, Ming Cheng, Hong Yu, Donghong Ji
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: graph construction, Relation, Relation Extraction, Sentence

abstract In this article, we describe our approach for the Bacteria Biotopes relation extraction (BB-rel) subtask in the BioNLP Shared Task 2019. This task aims to promote the development of text mining systems that extract relationships between Microorganism, Habitat and Phenotype entities. In this paper, we propose a novel approach for dependency graph construction based on lexical chains, so one dependency graph can represent one or multiple sentences. After that, we propose a neural network model which consists of the bidirectional long short-term memories and an attention graph convolution neural network to learn relation extraction features from the graph. Our approach is able to extract both intra- and inter-sentence relations, and meanwhile utilize syntax information. The results show that our approach achieved the best F1 (66.3{\%}) in the official evaluation participated by 7 teams.
Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature
Authors: Jihang Mao, Wanli Liu
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: BIG-bench Machine Learning, Medical Concept Normalization, Relation, Relation Extraction

In this paper, we present our participation in the Bacteria Biotope (BB) task at BioNLP-OST 2019. Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition, normalization and relation extraction. It achieves the state-of-the-art performance and is among the top two systems in five of all six subtasks.
CRAFT Shared Tasks 2019 Overview --- Integrated Structure, Semantics, and Coreference
Authors: William Baumgartner, Michael Bada, Sampo Pyysalo, Manuel R. Ciosici, Negacy Hailu, Harrison Pielke-Lombardo, Michael Regan, Lawrence Hunter
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Articles, coreference-resolution, Coreference Resolution, Dependency Parsing, Medical Named Entity Recognition, Sentence

As part of the BioNLP Open Shared Tasks 2019, the CRAFT Shared Tasks 2019 provides a platform to gauge the state of the art for three fundamental language processing tasks {---} dependency parse construction, coreference resolution, and ontology concept identification {---} over full-text biomedical articles. The structural annotation task requires the automatic generation of dependency parses for each sentence of an article given only the article text. The coreference resolution task focuses on linking coreferring base noun phrase mentions into chains using the symmetrical and transitive identity relation. The ontology concept annotation task involves the identification of concept mentions within text using the classes of ten distinct ontologies in the biomedical domain, both unmodified and augmented with extension classes. This paper provides an overview of each task, including descriptions of the data provided to participants and the evaluation metrics used, and discusses participant results relative to baseline performances for each of the three tasks.
UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition
Authors: Lenz Furrer, Joseph Cornelius, Fabio Rinaldi
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

As our submission to the CRAFT shared task 2019, we present two neural approaches to concept recognition. We propose two different systems for joint named entity recognition (NER) and normalization (NEN), both of which model the task as a sequence labeling problem. Our first system is a BiLSTM network with two separate outputs for NER and NEN trained from scratch, whereas the second system is an instance of BioBERT fine-tuned on the concept-recognition task. We exploit two strategies for extending concept coverage, ontology pretraining and backoff with a dictionary lookup. Our results show that the backoff strategy effectively tackles the problem of unseen concepts, addressing a major limitation of the chosen design. In the cross-system comparison, BioBERT proves to be a strong basis for creating a concept-recognition system, although some entity types are predicted more accurately by the BiLSTM-based system.
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering
Authors: Hai-Long Trieu, Anh-Khoa Duong Nguyen, Nhung Nguyen, Makoto Miwa, Hiroya Takamura, Sophia Ananiadou
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Articles, coreference-resolution, Coreference Resolution, Language Modeling, Language Modelling

This paper describes our system developed for the coreference resolution task of the CRAFT Shared Tasks 2019. The CRAFT corpus is more challenging than other existing corpora because it contains full text articles. We have employed an existing span-based state-of-theart neural coreference resolution system as a baseline system. We enhance the system with two different techniques to capture longdistance coreferent pairs. Firstly, we filter noisy mentions based on parse trees with increasing the number of antecedent candidates. Secondly, instead of relying on the LSTMs, we integrate the highly expressive language model{--}BERT into our model. Experimental results show that our proposed systems significantly outperform the baseline. The best performing system obtained F-scores of 44{\%}, 48{\%}, 39{\%}, 49{\%}, 40{\%}, and 57{\%} on the test set with B3, BLANC, CEAFE, CEAFM, LEA, and MUC metrics, respectively. Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.
Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task
Authors: Thang Minh Ngo, Jenna Kanerva, Filip Ginter, Sampo Pyysalo
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Dependency Parsing, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

We present the approach taken by the TurkuNLP group in the CRAFT Structural Annotation task, a shared task on dependency parsing. Our approach builds primarily on the Turku neural parser, a native dependency parser that ranked among the best in the recent CoNLL tasks on parsing Universal Dependencies. To adapt the parser to the biomedical domain, we considered and evaluated a number of approaches, including the generation of custom word embeddings, combination with other in-domain resources, and the incorporation of information from named entity recognition. We achieved a labeled attachment score of 89.7{\%}, the best result among task participants.
RDoC Task at BioNLP-OST 2019
Authors: Mohammad Anani, Nazmul Kazi, Matthew Kuntz, Kah, Indika a
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Information Retrieval, Retrieval, Sentence

BioNLP Open Shared Tasks (BioNLP-OST) is an international competition organized to facilitate development and sharing of computational tasks of biomedical text mining and solutions to them. For BioNLP-OST 2019, we introduced a new mental health informatics task called {``}RDoC Task{''}, which is composed of two subtasks: information retrieval and sentence extraction through National Institutes of Mental Health{'}s Research Domain Criteria framework. Five and four teams around the world participated in the two tasks, respectively. According to the performance on the two tasks, we observe that there is room for improvement for text mining on brain research and mental illness.
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension
Authors: Hangbo Bao, Li Dong, Furu Wei, Wenhui Wang, Nan Yang, Lei Cui, Songhao Piao, Ming Zhou
Date: 2019-11-01 00:00:00
Proceeding: WS 2019 11
Tasks: Machine Reading Comprehension, Reading Comprehension

Most machine reading comprehension (MRC) models separately handle encoding and matching with different network architectures. In contrast, pretrained language models with Transformer layers, such as GPT (Radford et al., 2018) and BERT (Devlin et al., 2018), have achieved competitive performance on MRC. A research question that naturally arises is: apart from the benefits of pre-training, how many performance gain comes from the unified network architecture. In this work, we evaluate and analyze unifying encoding and matching components with Transformer for the MRC task. Experimental results on SQuAD show that the unified model outperforms previous networks that separately treat encoding and matching. We also introduce a metric to inspect whether a Transformer layer tends to perform encoding or matching. The analysis results show that the unified model learns different modeling strategies compared with previous manually-designed models.