This study examined the use of neural word embeddings for clinical abbreviation disambiguation, a special case of word sense disambiguation (WSD). We investigated three different methods for deriving word embeddings from a large unlabeled clinical corpus: one existing method called Surrounding based embedding feature (SBE), and two newly developed methods: Left-Right surrounding based embedding feature (LR_SBE) and MAX surrounding based embedding feature (MAX_SBE). We then added these word embeddings as additional features to a Support Vector Machines (SVM) based WSD system. Evaluation using the clinical abbreviation datasets from both the Vanderbilt University and the University of Minnesota showed that neural word embedding features improved the performance of the SVMbased clinical abbreviation disambiguation system. More specifically, the new MAX_SBE method outperformed the other two methods and achieved the state-of-the-art performance on both clinical abbreviation datasets.

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Clinical Abbreviation Disambiguation Using Neural Word Embeddings

Acronyms are the short forms of phrases that facilitate conveying lengthy sentences in documents and serve as one of the mainstays of writing. Due to their importance, identifying acronyms and corresponding phrases (i.e., acronym identification (AI)) and finding the correct meaning of each acronym (i.e., acronym disambiguation (AD)) are crucial for text understanding. Despite the recent progress on this task, there are some limitations in the existing datasets which hinder further improvement. More specifically, limited size of manually annotated AI datasets or noises in the automatically created acronym identification datasets obstruct designing advanced high-performing acronym identification models. Moreover, the existing datasets are mostly limited to the medical domain and ignore other domains. In order to address these two limitations, we first create a manually annotated large AI dataset for scientific domain. This dataset contains 17,506 sentences which is substantially larger than previous scientific AI datasets. Next, we prepare an AD dataset for scientific domain with 62,441 samples which is significantly larger than previous scientific AD dataset. Our experiments show that the existing state-of-the-art models fall far behind human-level performance on both datasets proposed by this work. In addition, we propose a new deep learning model which utilizes the syntactical structure of the sentence to expand an ambiguous acronym in a sentence. The proposed model outperforms the state-of-the-art models on the new AD dataset, providing a strong baseline for future research on this dataset.

What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation

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Hybrid Email Spam Detection Model Using Artificial Intelligence

A common characteristic of content generated by healthcare professionals, regardless the actual clinical discipline or language, is the widespread and frequent use of abbreviations, acronyms, telegraphic phrases and shorthand notes. Despite the well-known issues related to the ambiguity and misinterpretation of abbreviations, their use in practice is required to simplify and enable communication-avoiding repetition of long complex specialized medical terminologies. Moreover, clinical texts typically do not provide explicit abbreviation definitions. Thus the performance of clinical natural language processing and text mining systems is significantly affected by the previous recognition and definition resolution of medical abbreviations. To promote the development of such key components, we have organized the second Biomedical Abbreviation Recognition and Resolution (BARR2) track. The overall aim of this effort was to evaluate strategies for detecting automatically mentions of abbreviations in running text, as well as returning their corresponding definition given the corresponding context from Spanish clinical case studies. For this track, we constructed the Spanish clinical case corpus (SPACCC). This collection was exhaustively annotated by hand by domain experts with abbreviation mentions together with their corresponding definitions, resulting in the BARR2 corpus. A total of 5 teams submitted 26 runs for the two BARR2 subtasks: (a) the detection of explicit occurrences of abbreviation-definition pairs and (b) the resolution of abbreviations regardless whether their definition is mentioned within the actual document. Here we summarize the BARR2 track setting, the obtained results and the methodologies used by participating systems. The BARR2 task summary, resources and evaluation tool for testing systems beyond this campaign are available at: http://temu.bsc.es/BARR2.

/pdf/finding-mentions-of-abbreviations-and-their-definitions-in-2jiaozh3cu.pdf

Finding Mentions of Abbreviations and Their Definitions in Spanish Clinical Cases: The BARR2 Shared Task Evaluation Results.

Acronyms are abbreviations formed from the initial components of words or phrases. In enterprises, people often use acronyms to make communications more efficient. However, acronyms could be difficult to understand for people who are not familiar with the subject matter (new employees, etc.), thereby affecting productivity. To alleviate such troubles, we study how to automatically resolve the true meanings of acronyms in a given context. Acronym disambiguation for enterprises is challenging for several reasons. First, acronyms may be highly ambiguous since an acronym used in the enterprise could have multiple internal and external meanings. Second, there are usually no comprehensive knowledge bases such as Wikipedia available in enterprises. Finally, the system should be generic to work for any enterprise. In this work we propose an end-to-end framework to tackle all these challenges. The framework takes the enterprise corpus as input and produces a high-quality acronym disambiguation system as output. Our disambiguation models are trained via distant supervised learning, without requiring any manually labeled training examples. Therefore, our proposed framework can be deployed to any enterprise to support high-quality acronym disambiguation. Experimental results on real world data justified the effectiveness of our system.

/pdf/guess-me-if-you-can-acronym-disambiguation-for-enterprises-71wrtzy82y.pdf

Guess Me if You Can: Acronym Disambiguation for Enterprises.

According to the website AcronymFinder.com which is one of the world's largest and most comprehensive dictionaries of acronyms, an average of 37 new human-edited acronym definitions are added every day. There are 379,918 acronyms with 4,766,899 definitions on that site up to now, and each acronym has 12.5 definitions on average. It is a very important research topic to identify what exactly an acronym means in a given context for document comprehension as well as for document retrieval. In this paper, we propose two word embedding based models for acronym disambiguation. Word embedding is to represent words in a continuous and multidimensional vector space, so that it is easy to calculate the semantic similarity between words by calculating the vector distance. We evaluate the models on MSH Dataset and ScienceWISE Dataset, and both models outperform the state-of-art methods on accuracy. The experimental results show that word embedding helps to improve acronym disambiguation.

https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9404/9721

Chao Li

Papers

Acronym disambiguation using word embedding