A Resource for Detecting Misspellings and Denoising Medical Text Data
Authors: Enrico Mensa, Gian Manuel Marino, Davide Colla, Matteo Delsanto, Daniele P. Radicioni (Università di Torino, 2020)
Topic: In-domain IR and IE

Abstract
In this paper we propose a method for collecting a dictionary to deal with noisy medical text documents. The quality of such Italian Emergency Room Reports is so poor that in most cases these can be hardly automatically elaborated; this also holds for other languages (e.g., English), with the notable difference that no Italian dictionary has been proposed to deal with this jargon. In this work we introduce and evaluate a resource designed to fill this gap.

Introduction
Noise in textual data is a very common phenomenon afflicting text documents, especially when dealing with informal texts such as chats, SMS and e-mails. This kind of text inherently contains spelling errors, special characters, nonstandard word forms, grammar mistakes, and so on (Liu et al., 2012). In this work we focus on a type of text which can also be very noisy: emergency room reports. In the broader frame of a project aimed at detecting injuries stemming from violence acts in narrative texts contained in emergency room reports, we recently developed the VIDES, so dubbed after ‘Violence Detection System’ (Mensa et al., 2020). This system is concerned with categorizing textual descriptions as containing violence-related injuries (V) vs. nonviolence-related injuries (NV), which is a relevant task to the ends of devising alerting mechanisms to track and prevent violence episodes. VIDES combines a neural architecture which performs the categorization step (thus discriminating V and NV records) and a Framenet-based approach, whereby semantic roles are represented through a synthetic description employing a set of word embeddings.2 More specifically, a model of violent event has been devised: records that are recognized as containing violence-related injuries are further processed by an explanation module, which is charged to individuate the main elements corroborating that categorization (V) by identifying the involved agent, the type of injury, the involved body district etc.. Explaining the categorization ultimately involves filling the semantic components of the violence frame. All such elements contribute to recognizing a violent event as the source of the injuries complained by ER patients. During the development of VIDES we realized that in order to run sophisticated algorithms for the detection and extraction of such violent traits we needed to cope with the noise contained in the input medical records. Some efforts have been invested to deal with different sorts of linguistic phenomena menacing the comprehension of texts; however, most existing works are focused on the English language, and rely on dictionaries that cannot be directly employed on Italian text documents. In this preliminary work we start to tackle the issue of noisy words in medical records for Italian texts, by specifically focusing on misspellings. Our contribution is twofold: we first manually explore the dataset by analyzing a small sample of records in order to determine whether the main traits and issues present in other languages are also shared by Italian reports; secondly, we collect, merge and evaluate a set of Italian dictionaries, which constitute a brick fundamental to build any domain specific spell-checking algorithm (LópezHernández et al., 2019).

Conclusion
In this work we tackled the issue of detecting textual noise in Italian room emergency reports, focusing specifically on misspellings. Firstly we examined the reports and found out that the sorts of issues reported in literature for other languages can also be found in Italian text documents. Secondly, we developed and evaluated an Italian dictionary suited for the task of noise detection. In future work we plan to expand the dictionary by including the terms from the Italian ICD-9 and ICD-10 (International Classification of Diseases), that may be useful to interpret acronyms and resolve abbreviations. Moreover, we plan to employ this dictionary in a fully fledged spell-checking system. Finally, the usage of semantic —sense indexed— representations such as, e.g., (Mensa et al., 2018) and (Colla et al., 2020a; Colla et al., 2020b) will be explored, in order to deal with real word mistakes, and more in general contextual information (Basile et al., 2019) will be considered as a main cue in order to uncover and correct this sort of errors. For example, by leveraging the terminology surrounding noisy tokens we plan to distinguish the more scattered misspellings from the other terms that are not present in our dictionary.