Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text

Xu, Jing; Gan, Lu; Cheng, Mian; Wu, Quanyuan

doi:10.1155/2018/2548537

Cited by 18 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xu et al [150] have proposed an unsupervised method to detect boundaries and classify medical entities mentioned in a medical Chinese text, and link mentions to their entities. Initially, the method exploits the part-of-speech and dependency relations, and maps the text to concepts in offline and online lexical resources to detect mentions of medical entities.…”

Section: Rule-based Methodsmentioning

confidence: 99%

“…Thus, they do an exact matching for these terms to annotate a maximum number of unambiguous entities to partially solve the ambiguity. Xu et al [150] have benefited from more context to solve the ambiguity problem by using categories representation by Word2vec, PoS, dependency relation and semantic correlation knowledge. However, the method may miss some medical entities in the non-medical terms filtering step.…”

Section: Ambiguitymentioning

confidence: 99%

See 1 more Smart Citation

Information extraction from electronic medical documents: state of the art and future research directions

2022

View full text Add to dashboard Cite

In the medical field, a doctor must have a comprehensive knowledge by reading and writing narrative documents, and he is responsible for every decision he takes for patients. Unfortunately, it is very tiring to read all necessary information about drugs, diseases and patients due to the large amount of documents that are increasing every day. Consequently, so many medical errors can happen and even kill people. Likewise, there is such an important field that can handle this problem, which is the information extraction. There are several important tasks in this field to extract the important and desired information from unstructured text written in natural language. The main principal tasks are named entity recognition and relation extraction since they can structure the text by extracting the relevant information. However, in order to treat the narrative text we should use natural language processing techniques to extract useful information and features. In our paper, we introduce and discuss the several techniques and solutions used in these tasks. Furthermore, we outline the challenges in information extraction from medical documents. In our knowledge, this is the most comprehensive survey in the literature with an experimental analysis and a suggestion for some uncovered directions.

show abstract

Section: Rule-based Methodsmentioning

confidence: 99%

Section: Ambiguitymentioning

confidence: 99%

Information extraction from electronic medical documents: state of the art and future research directions

2022

View full text Add to dashboard Cite

show abstract

“…Candidate ranking could be done in either a supervised way (Chen and Ji, 2011;Gupta et al, 2017;Kolitsas et al, 2018) or an unsupervised way (Cucerzan, 2007;Chen et al, 2010;Xu et al, 2018). Potential features for ranking include surface names, popularity, types of the entities, and the context surrounding the mention and the entities (Shen et al, 2015).…”

Section: Entity Linkingmentioning

confidence: 99%

Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems

Lertvittayakumjorn¹,

Bonadiman²,

Mansour³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of "cheese pizza" (a menu item) and "oreo cookies" (a topping) from an input utterance "Can I order a cheese pizza with oreo cookies on top?" exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements.

show abstract

“…The 2015-2016 tasks focus on information retrieval in biomedical domains. Xu et al 15 introduce a model to identify cross-lingual candidates for concept normalization using a character-based neural translation model trained on a multilingual biomedical terminology. They use UMLS data in Spanish, French, Dutch, and German.…”

Section: Introductionmentioning

confidence: 99%

Cross-lingual Unified Medical Language System entity linking in online health communities

Bitton

Cohen

Schifter

et al. 2020

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

Objective In Hebrew online health communities, participants commonly write medical terms that appear as transliterated forms of a source term in English. Such transliterations introduce high variability in text and challenge text-analytics methods. To reduce their variability, medical terms must be normalized, such as linking them to Unified Medical Language System (UMLS) concepts. We present a method to identify both transliterated and translated Hebrew medical terms and link them with UMLS entities. Materials and Methods We investigate the effect of linking terms in Camoni, a popular Israeli online health community in Hebrew. Our method, MDTEL (Medical Deep Transliteration Entity Linking), includes (1) an attention-based recurrent neural network encoder-decoder to transliterate words and mapping UMLS from English to Hebrew, (2) an unsupervised method for creating a transliteration dataset in any language without manually labeled data, and (3) an efficient way to identify and link medical entities in the Hebrew corpus to UMLS concepts, by producing a high-recall list of candidate medical terms in the corpus, and then filtering the candidates to relevant medical terms. Results We carry out experiments on 3 disease-specific communities: diabetes, multiple sclerosis, and depression. MDTEL tagging and normalizing on Camoni posts achieved 99% accuracy, 92% recall, and 87% precision. When tagging and normalizing terms in queries from the Camoni search logs, UMLS-normalized queries improved search results in 46% of the cases. Conclusions Cross-lingual UMLS entity linking from Hebrew is possible and improves search performance across communities. Annotated datasets, annotation guidelines, and code are made available online (https://github.com/yonatanbitton/mdtel).

show abstract

Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text

Cited by 18 publications

References 18 publications

Information extraction from electronic medical documents: state of the art and future research directions

Information extraction from electronic medical documents: state of the art and future research directions

Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems

Cross-lingual Unified Medical Language System entity linking in online health communities

Contact Info

Product

Resources

About