21,798 research outputs found

    GNTeam at 2018 n2c2:Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries

    Get PDF
    Monitoring the administration of drugs and adverse drug reactions are key parts of pharmacovigilance. In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, and adverse events) from hospital discharge summaries through deep learning that relies on various representations for clinical named entity recognition. This work was officially part of the 2018 n2c2 shared task, and we use the data supplied as part of the task. We developed two deep learning architecture based on recurrent neural networks and pre-trained language models. We also explore the effect of augmenting word representations with semantic features for clinical named entity recognition. Our feature-augmented BiLSTM-CRF model performed with F1-score of 92.67% and ranked 4th for entity extraction sub-task among submitted systems to n2c2 challenge. The recurrent neural networks that use the pre-trained domain-specific word embeddings and a CRF layer for label optimization perform drug, adverse event and related entities extraction with micro-averaged F1-score of over 91%. The augmentation of word vectors with semantic features extracted using available clinical NLP toolkits can further improve the performance. Word embeddings that are pre-trained on a large unannotated corpus of relevant documents and further fine-tuned to the task perform rather well. However, the augmentation of word embeddings with semantic features can help improve the performance (primarily by boosting precision) of drug-related named entity recognition from electronic health records

    Boosting with Incomplete Information

    Get PDF
    In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we present a boosting approach that integrates features with incomplete information and those with complete information to form a strong classifier. By introducing hidden variables to model missing information, we form loss functions that combine fully labeled data with partially labeled data to effectively learn normalized and unnormalized models. The primal problems of the proposed optimization problems with these loss functions are provided to show their close relationship and the motivations behind them. We use auxiliary functions to bound the change of the loss functions and derive explicit parameter update rules for the learning algorithms. We demonstrate encouraging results on two real-world problems — visual object recognition in computer vision and named entity recognition in natural language processing — to show the effectiveness of the proposed boosting approach

    MLNet: a multi-level multimodal named entity recognition architecture

    Get PDF
    In the field of human–computer interaction, accurate identification of talking objects can help robots to accomplish subsequent tasks such as decision-making or recommendation; therefore, object determination is of great interest as a pre-requisite task. Whether it is named entity recognition (NER) in natural language processing (NLP) work or object detection (OD) task in the computer vision (CV) field, the essence is to achieve object recognition. Currently, multimodal approaches are widely used in basic image recognition and natural language processing tasks. This multimodal architecture can perform entity recognition tasks more accurately, but when faced with short texts and images containing more noise, we find that there is still room for optimization in the image-text-based multimodal named entity recognition (MNER) architecture. In this study, we propose a new multi-level multimodal named entity recognition architecture, which is a network capable of extracting useful visual information for boosting semantic understanding and subsequently improving entity identification efficacy. Specifically, we first performed image and text encoding separately and then built a symmetric neural network architecture based on Transformer for multimodal feature fusion. We utilized a gating mechanism to filter visual information that is significantly related to the textual content, in order to enhance text understanding and achieve semantic disambiguation. Furthermore, we incorporated character-level vector encoding to reduce text noise. Finally, we employed Conditional Random Fields for label classification task. Experiments on the Twitter dataset show that our model works to increase the accuracy of the MNER task

    Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition

    Full text link
    We describe the CoNLL-2002 shared task: language-independent named entity recognition. We give background information on the data sets and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance.Comment: 4 page

    Implementing universal dependency, morphology, and multiword expression annotation standards for Turkish language processing

    Get PDF
    Released only a year ago as the outputs of a research project (“Parsing Web 2.0 Sentences”, supported in part by a TUBİTAK 1001 grant (No. 112E276) and a part of the ICT COST Action PARSEME (IC1207)), IMST and IWT are currently the most comprehensive Turkish dependency treebanks in the literature. This article introduces the final states of our treebanks, as well as a newly integrated hierarchical categorization of the multiheaded dependencies and their organization in an exclusive deep dependency layer in the treebanks. It also presents the adaptation of recent studies on standardizing multiword expression and named entity annotation schemes for the Turkish language and integration of benchmark annotations into the dependency layers of our treebanks and the mapping of the treebanks to the latest Universal Dependencies (v2.0) standard, ensuring further compliance with rising universal annotation trends. In addition to significantly boosting the universal recognition of Turkish treebanks, our recent efforts have shown an improvement in their syntactic parsing performance (up to 77.8%/82.8% LAS and 84.0%/87.9% UAS for IMST/IWT, respectively). The final states of the treebanks are expected to be more suited to different natural language processing tasks, such as named entity recognition, multiword expression detection, transfer-based machine translation, semantic parsing, and semantic role labeling.Peer reviewe
    • …
    corecore