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Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints 22

Using Bottleneck Adapters to Identify Cancer in Clinical Not...

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22nd workshop on Biomedical natural language processing and BioNLP Shared Tasks, BioNLP 2023

作者： Rohanian, Omid Jauncey, Hannah Nouriborji, Mohammadmahdi Chauhan, Vinod Kumar Gonçalves, Bronner P. Kartsonaki, Christiana Merson, Laura Clifton, David Department of Engineering Science University of Oxford Oxford United Kingdom ISARIC Pandemic Sciences Institute University of Oxford Oxford United Kingdom University of Oxford United Kingdom Oxford-Suzhou Centre for Advanced Research Suzhou China Sharif University of Technology Tehran Iran NLPie Research Oxford United Kingdom

ISBN: (纸本)9781959429852

processing information locked within clinical health records is a challenging task that remains an active area of research in biomedical NLP. In this work, we evaluate a broad set of machine learning techniques ranging from simple RNNs to specialised transformers such as BioBERT on a dataset containing clinical notes along with a set of annotations indicating whether a sample is cancer-related or not. Furthermore, we specifically employ efficient fine-tuning methods from NLP, namely, bottleneck adapters and prompt tuning, to adapt the models to our specialised task. Our evaluations suggest that fine-tuning a frozen BERT model pre-trained on natural language and with bottleneck adapters outperforms all other strategies, including full fine-tuning of the specialised BioBERT model. based on our findings, we suggest that using bottleneck adapters in low-resource situations with limited access to labelled data or processing capacity could be a viable strategy in biomedical text mining. The code used in the experiments are going to be made available at [LINK ANONYMIZED]. © 2023 Association for Computational Linguistics.

关键词： natural language processing systems

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graph methods for multilingual framenets 11

Graph methods for multilingual framenets

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11th workshop on graph-based methods for natural language processing, Textgraphs 2017, in conjunction with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Baker, Collin F. Ellsworth, Michael International Computer Science Institute 1947 Center St. BerkeleyCA94704 United States

ISBN: (纸本)9781945626609

This paper introduces a new, graph-based view of the data of the FrameNet project, which we hope will make it easier to understand the mixture of semantic and syntactic information contained in FrameNet annotation. We show how English FrameNet and other Frame Semantic resources can be represented as sets of interconnected graphs of frames, frame elements, semantic types, and annotated instances of them in text. We display examples of the new graphical representation based on the annotations, which combine Frame Semantics and Construction Grammar, thus capturing most of the syntax and semantics of each sentence. We consider how graph theory could help researchers to make better use of FrameNet data for tasks such as automatic Frame Semantic role labeling, paraphrasing, and translation. Finally, we describe the development of FrameNet-like lexical resources for other languages in the current Multilingual FrameNet project. which seeks to discover cross-lingual alignments, both in the lexicon (for frames and lexical units within frames) and across parallel or comparable texts. We conclude with an example showing graphically the semantic and syntactic similarities and differences between parallel sentences in English and Japanese. We will release software for displaying such graphs from the current data releases. © 2017 Association for Computational Linguistics

关键词： graph theory

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Web Attack Payload Identification and Interpretability Analysis based on graph Convolutional Network

Web Attack Payload Identification and Interpretability Analy...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yijia Xu Yong Fang Zhonglin Liu Cyber Science and Engineering College Sichuan University Chengdu Sichuan China

Web attack payload identification is a significant part of the Web defense system. The current Web attack payload identification usually combines natural language processing and deep learning to automatically build a detection model to intercept malicious payloads. However, these detection methods ignore the bidirectional association between fields and is prone to the payload dilution problem for long strings. In addition, the weak interpretability of deep learning models makes it difficult for researchers to solve the problem of model pollution and adjust the model according to the prediction logic. Therefore, this paper proposes a new Web attack payload identification method based on graph Convolutional Network (GCN), which can effectively extract Web payload features and help model interpretability analysis. The core of this method is to transform the text feature problem into a graph feature extraction problem and to understand the structure and content of the Web payload from the graph perspective. The method performs node embedding on the Web payload graph through GCN, then converts the embedding vector into a graph feature vector through a feature fusion method. The node ablation method is used to analyze malicious payloads' interpretability and calculate the predicted impact rate of nodes inside the graph structure. The experiments on the CSIC 2010 v2 HTTP dataset show that the method proposed in this paper has high accuracy for identifying Web attack payloads, and the node embedding of the Relational graph Convolutional Network (RGCN) method is more suitable for identifying Web attack payloads than other GCN methods. The research results of the paper show that the model interpretability analysis based on the Web payload graph is reasonable and can effectively assist researchers in adjusting the model and preventing the problem of model pollution.

关键词： Deep learning Analytical models Pollution Transforms Predictive models Feature extraction natural language processing

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Improving Unsupervised Extractive Summarization with Facet-Aware Modeling

Improving Unsupervised Extractive Summarization with Facet-A...

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Joint Conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint Conference on natural language processing (IJCNLP) / 6th workshop on Representation Learning for NLP (RepL4NLP)

作者： Liang, Xinnian Wu, Shuangzhi Li, Mu Li, Zhoujun Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Tencent Cloud Xiaowei Beijing Peoples R China

ISBN: (纸本)9781954085541

Unsupervised extractive summarization aims to extract salient sentences from documents without labeled corpus. Existing methods are mostly graph-based by computing sentence centrality. These methods usually tend to select sentences within the same facet, however, which often leads to the facet bias problem especially when the document has multiple facets (i.e. long-document and multidocuments). To address this problem, we proposed a novel facet-aware centrality-based ranking model. We let the model pay more attention to different facets by introducing a sentence-document weight. The weight is added to the sentence centrality score. We evaluate our method on a wide range of summarization tasks that include 8 representative benchmark datasets. Experimental results show that our method consistently outperforms strong baselines especially in long and multi-document scenarios and even performs comparably to some supervised models. Extensive analyses confirm that the performance gains come from alleviating the facet bias problem.

关键词： graphic methods

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A Framework for Intelligent Building Information Spoken Dialogue System (iBISDS) 28

A Framework for Intelligent Building Information Spoken Dial...

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28th International workshop on Intelligent Computing in Engineering of the European Group for Intelligent Computing in Engineering, EG-ICE 2021

作者： Wang, Ning Issa, Raja R.A. Anumba, Chimay J. University of Florida United States

ISBN: (纸本)9783798332126

Existing Building Information Modeling (BIM) information extraction (IE) methods require users to spend more time learning different query languages and database structures, which is difficult for non-BIM experts. natural language-based IE from building information models is required by both BIM experts and non-experts. Conversational Artificial Intelligence (CAI) technologies improve the generation of speech-based IE from databases. However, existing research on speech-based IE from building information models is limited. Therefore, this research develops a framework for intelligent Building Information Spoken Dialogue System (iBISDS) to achieve speech-based IE from building information models. This study is focused on extracting attribute information of building components. The iBISDS is a speech-based question answering (QA) system that can provide information support for on-site and off-site construction project team members. The iBISDS framework will facilitate the further adoption of CAI technologies in the construction area. © 2021 Universitätsverlag der Technischen Universität Berlin. All Rights Reserved.

关键词： Query processing

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Overview of Deep Learning based Cardiac MR Image Segmentation methods

Overview of Deep Learning Based Cardiac MR Image Segmentatio...

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Li, Bingjie Miao, Jianyu Yang, Tiejun Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Peoples R China Henan Univ Technol Coll Artificial Intelligence & Big Data Zhengzhou Peoples R China

ISBN: (纸本)9781450391870

Cardiac image segmentation has great significance of making correct diagnoses of cardiac diseases, and providing useful information for clinical treatment and surgery. Among cardiac images, magnetic resonance imaging (MRI) is the most common modality. With the rapid development of deep learning, it is widely used in various fields such as computer vision, natural language processing and visual recognition. In the meantime, a large number of deep learning based methods have been proposed for Cardiac MR image segmentation. In this paper, we attempt to provide a survey on such methods published in peer-reviewed journals and conferences between 2017 and 2021, including their motivations and segmentation strategies. We then explore the advantages, disadvantages and the challenges such as generalization ability, interpretability and so on. In addition, several publicly available cardiac image datasets and evaluation metrics are given. This paper will offer useful guidelines to study MR image segmentation for researchers and practitioners working in related domains.

关键词： deep learning cardiac MR image segmentation

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Aspect based Sentiment Analysis: Movie and Television Series reviews

Aspect Based Sentiment Analysis: Movie and Television Series...

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International workshop on Advanced Imaging Technology (IWAIT)

作者： Cooray, Thavisha Perera, Geethika Kugathasan, Archchana Alosius, Jesuthasan Sri Lanka Inst Informat Technol New Kandy Rd Malabe Sri Lanka

ISBN: (数字)9781510643659

ISBN: (纸本)9781510643659

Aspect based sentiment analysis (ABSA) is a methodology used in various fields for analyzing the reviews of customers to project an overall opinion on any different product or service. With the evolution of the internet, people are provided with a time saving cheap procedure like this, to express viewpoints to a mass audience. With the intension of gaining market value, numerous fields are bestowed the chance in collecting information from the internet, free of charge. The application of Machine Learning (ML) methods for the procedure of evaluation regarding aspects connected to TV series and movies, has not been initiated. This would be a novel progression for the film industry. Research under this study focuses on implementing ABSA on TV series or movies based on sub aspects of genre as well as sub aspects of cast and crew. As the initial step, web scraping can be used for data collection from social media. Next, data is processed to generate adequate results for anyone who needs a broader understanding on significance of a TV series or a movie related to the aspects mentioned earlier. To collect information belonging to each aspect is further analyzed. The accuracy of the results of the suggested system has been gained high as 79% and above. In conclusion, the accuracy of the solution is greatly successful compared to the previous tasks with high market value.

关键词： Sentiment Analysis Aspect based Sentiment Analysis natural language processing Sentiment Polarity Aspect Category Support Vector Machine Artificial Neural Network

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2nd workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2021) 21

2nd Workshop on Patent Text Mining and Semantic Technologies...

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44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Krestel, Ralf Aras, Hidir Andersson, Linda Piroi, Florina Hanbury, Allan Alderucci, Dean Univ Potsdam Hasso Plattner Inst Potsdam Germany FIZ Karlsruhe Eggenstein Leopoldshafen Germany Artificial Researcher IT GmbH Vienna Austria Data Sci Studio Vienna Austria TU Wien Vienna Austria Carnegie Mellon Univ Ctr AI & Patent Anal Pittsburgh PA 15213 USA

ISBN: (纸本)9781450380379

Information retrieval plays a crucial role in the patent domain. With the success of deep learning (DL) in other domains, patent practitioners and researchers are increasingly developing DL-based approaches to support experts in the patenting process or to automate processes for patent analysis. AI-enhanced information retrieval systems can improve patent search but also require lots of annotated data. When working with patent data, particular challenges arise that call for adaption and novel approaches of general IR and AI methods. with this workshop series we want to establish a two-way communication channel between industry and academia from relevant fields in information retrieval, such as natural language processing (NLP), text and data mining (TDM), and semantic technologies (ST), in order to explore and transfer new knowledge, methods and technologies for the benefit of industrial applications as well as support interdisciplinary research in applied sciences for the intellectual property (IP) and neighbouring domains.

关键词： patent analysis text mining semantic technology deep learning

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Automatic Generation of Russian News Headlines

Automatic Generation of Russian News Headlines

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2021 International Conference "Internet and Modern Society", IMS 2021

作者： Tretiak, Ekaterina Saint Petersburg State University 7-9 Universitetskaya emb. St Petersburg199034 Russia

Text summarization is one of the key natural language processing tasks. Automated text summarization has the potential to save time when creating reviews, abstracts etc. of the texts across multiple domains. Automatic headline generation is a challenging kind of text summarization. Basically, the distinction between extractive and abstractive summarization methods is drawn. Application of the extractive summarization techniques results in the extraction of relevant words or sentences from the original text. Abstractive summarization models synthesize a summary in which some of its material is not present in the input document. This paper deals with the fine-tuning the pretrained model based on Transformer architecture for the task of generation of Russian news headlines. Experiments discussed were carried out for the new dataset of Russian news which was automatically compiled from the "Bumaga" website. The paper contains the quantitative evaluation results using BLEU und ROUGE metrics as well as the human evaluation results. Finally, the paper presents error analysis and discussion of particular contexts. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： natural language processing systems

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Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing 5

Exploring Text-to-Text Transformers for English to Hinglish ...

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5th workshop on Computational Approaches to Linguistic Code-Switching, CALCS 2021

作者： Jawahar, Ganesh Billah Nagoudi, El Moatez Abdul-Mageed, Muhammad Lakshmanan, Laks V.S. Natural Language Processing Lab University of British Columbia Canada Department of Computer Science University of British Columbia Canada

ISBN: (纸本)9781954085459

We describe models focused at the understudied problem of translating between monolingual and code-mixed language pairs. More specifically, we offer a wide range of models that convert monolingual English text into Hinglish (code-mixed Hindi and English). Given the recent success of pretrained language models, we also test the utility of two recent Transformer-based encoder-decoder models (i.e., mT5 and mBART) on the task finding both to work well. Given the paucity of training data for code-mixing, we also propose a dependency-free method for generating code-mixed texts from bilingual distributed representations that we exploit for improving language model performance. In particular, armed with this additional data, we adopt a curriculum learning approach where we first finetune the language models on synthetic data then on gold code-mixed data. We find that, although simple, our synthetic code-mixing method is competitive with (and in some cases is even superior to) several standard methods (backtranslation, method based on equivalence constraint theory) under a diverse set of conditions. Our work shows that the mT5 model, finetuned following the curriculum learning procedure, achieves best translation performance (12.67 BLEU). Our models place first in the overall ranking of the English-Hinglish official shared task. ©2021 Association for Computational Linguistics

关键词： Curricula

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