Semantically factoid question answering using fuzzy SVM Named Entity Recognition

In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B , I , O only) compared to multiple entity-classes in traditional NER (i.e., B entity 1, B entity 2, I entity 1, I , O ), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.

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A pre-training and self-training approach for biomedical named entity recognition

PLoS ONE ◽

10.1371/journal.pone.0246310 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246310

Author(s):

Shang Gao ◽

Olivera Kotevska ◽

Alexandre Sorokine ◽

J. Blair Christian

Keyword(s):

Transfer Learning ◽

Question Answering ◽

Named Entity Recognition ◽

Model Performance ◽

Training Data ◽

Entity Recognition ◽

Literature Mining ◽

Unified Medical Language System ◽

Named Entity ◽

Training Approach

Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.

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Question Generation for French: Collating Parsers and Paraphrasing Questions

Dialogue & Discourse ◽

10.5087/dad.2012.203 ◽

2012 ◽

Vol 3 (2) ◽

pp. 43-74 ◽

Cited By ~ 7

Author(s):

Delphine Bernhard ◽

Louis de Viron ◽

Véronique Moriceau ◽

Xavier Tannier

Keyword(s):

Question Answering ◽

Named Entity Recognition ◽

Entity Recognition ◽

Generation System ◽

Question Generation ◽

Named Entity ◽

Question Analysis ◽

Medium Quality ◽

Online Encyclopedia ◽

Action Verbs

This article describes a question generation system for French. The transformation of declarative sentences into questions relies on two different syntactic parsers and named entity recognition tools. This makes it possible to further diversify the questions generated and to possibly alleviate the problems inherent to the analysis tools. The system also generates reformulations for the questions based on variations in the question words, inducing answers with different granularities, and nominalisations of action verbs. We evaluate the questions generated for sentences extracted from two different corpora: a corpus of newspaper articles used for the CLEF Question Answering evaluation campaign and a corpus of simplified online encyclopedia articles. The evaluation shows that the system is able to generate a majority of good and medium quality questions. We also present an original evaluation of the question generation system using the question analysis module of a question answering system.

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