Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT

We hypothesize that large language models (LLMs) based on the transformer architecture can enable automated detection of clinical phenotype terms, including terms not documented in the HPO. In this study, we developed two types of models: PhenoBCBERT, a BERT-based model, utilizing Bio+Clinical BERT as its pre-trained model, and PhenoGPT, a GPT-based model that can be initialized from diverse GPT models, including open-source versions such as GPT-J, Falcon, and LLaMA, as well as closed-source versions such as GPT-3 and GPT-3.5. We compared our methods with PhenoTagger, a recently developed HPO recognition tool that combines rule-based and deep learning methods. We found that our methods can extract more phenotype concepts, including novel ones not characterized by HPO. We also performed case studies on biomedical literature to illustrate how new phenotype information can be recognized and extracted. We compared current BERT-based versus GPT-based models for phenotype tagging, in multiple aspects including model architecture, memory usage, speed, accuracy, and privacy protection. We also discussed the addition of a negation step and an HPO normalization layer to the transformer models for improved HPO term tagging. In conclusion, PhenoBCBERT and PhenoGPT enable the automated discovery of phenotype terms from clinical notes and biomedical literature, facilitating automated downstream tasks to derive new biological insights on human diseases

Context-sensitive Spelling Correction Using Google Web 1T 5-Gram Information

In computing, spell checking is the process of detecting and sometimes providing spelling suggestions for incorrectly spelled words in a text. Basically, a spell checker is a computer program that uses a dictionary of words to perform spell checking. The bigger the dictionary is, the higher is the error detection rate. The fact that spell checkers are based on regular dictionaries, they suffer from data sparseness problem as they cannot capture large vocabulary of words including proper names, domain-specific terms, technical jargons, special acronyms, and terminologies. As a result, they exhibit low error detection rate and often fail to catch major errors in the text. This paper proposes a new context-sensitive spelling correction method for detecting and correcting non-word and real-word errors in digital text documents. The approach hinges around data statistics from Google Web 1T 5-gram data set which consists of a big volume of n-gram word sequences, extracted from the World Wide Web. Fundamentally, the proposed method comprises an error detector that detects misspellings, a candidate spellings generator based on a character 2-gram model that generates correction suggestions, and an error corrector that performs contextual error correction. Experiments conducted on a set of text documents from different domains and containing misspellings, showed an outstanding spelling error correction rate and a drastic reduction of both non-word and real-word errors. In a further study, the proposed algorithm is to be parallelized so as to lower the computational cost of the error detection and correction processes.Comment: LACSC - Lebanese Association for Computational Sciences - http://www.lacsc.or

English spelling and the computer

The first half of the book is about spelling, the second about computers. Chapter Two describes how English spelling came to be in the state that it’s in today. In Chapter Three I summarize the debate between those who propose radical change to the system and those who favour keeping it as it is, and I show how computerized correction can be seen as providing at least some of the benefits that have been claimed for spelling reform. Too much of the literature on computerized spellcheckers describes tests based on collections of artificially created errors; Chapter Four looks at the sorts of misspellings that people actually make, to see more clearly the problems that a spellchecker has to face. Chapter Five looks more closely at the errors that people make when they don’t know how to spell a word, and Chapter Six at the errors that people make when they know perfectly well how to spell a word but for some reason write or type something else. Chapter Seven begins the second part of the book with a description of the methods that have been devised over the last thirty years for getting computers to detect and correct spelling errors. Its conclusion is that spellcheckers have some way to go before they can do the job we would like them to do. Chapters Eight to Ten describe a spellchecker that I have designed which attempts to address some of the remaining problems, especially those presented by badly spelt text. In 1982, when I began this research, there were no spellcheckers that would do anything useful with a sentence such as, ‘You shud try to rember all ways to youz a lifejacket when yotting.’ That my spellchecker corrects this perfectly (which it does) is less impressive now, I have to admit, than it would have been then, simply because there are now a few spellcheckers on the market which do make a reasonable attempt at errors of that kind. My spellchecker does, however, handle some classes of errors that other spellcheckers do not perform well on, and Chapter Eleven concludes the book with the results of some comparative tests, a few reflections on my spellchecker’s shortcomings and some speculations on possible developments

An improved Levenshtein algorithm for spelling correction word candidate list generation

Candidates’ list generation in spelling correction is a process of finding words from a lexicon that should be close to the incorrect word. The most widely used algorithm for generating candidates’ list for incorrect words is based on Levenshtein distance. However, this algorithm takes too much time when there is a large number of spelling errors. The reason is that calculating Levenshtein algorithm includes operations that create an array and fill the cells of this array by comparing the characters of an incorrect word with the characters of a word from a lexicon. Since most lexicons contain millions of words, then these operations will be repeated millions of times for each incorrect word to generate its candidates list. This dissertation improved Levenshtein algorithm by designing an operational technique that has been included in this algorithm. The proposed operational technique enhances Levenshtein algorithm in terms of the processing time of its executing without affecting its accuracy. It reduces the operations required to measure cells’ values in the first row, first column, second row, second column, third row, and third column in Levenshtein array. The improved Levenshtein algorithm was evaluated against the original algorithm. Experimental results show that the proposed algorithm outperforms Levenshtein algorithm in terms of the processing time by 36.45% while the accuracy of both algorithms is still the same

Studying the Effect and Treatment of Misspelled Queries in Cross-Language Information Retrieval

[Abstract] The performance of Information Retrieval systems is limited by the linguistic variation present in natural language texts. Word-level Natural Language Processing techniques have been shown to be useful in reducing this variation. In this article, we summarize our work on the extension of these techniques for dealing with phrase-level variation in European languages, taking Spanish as a case in point. We propose the use of syntactic dependencies as complex index terms in an attempt to solve the problems deriving from both syntactic and morpho-syntactic variation and, in this way, to obtain more precise index terms. Such dependencies are obtained through a shallow parser based on cascades of finite-state transducers in order to reduce as far as possible the overhead due to this parsing process. The use of different sources of syntactic information, queries or documents, has been also studied, as has the restriction of the dependencies applied to those obtained from noun phrases. Our approaches have been tested using the CLEF corpus, obtaining consistent improvements with regard to classical word-level non-linguistic techniques. Results show, on the one hand, that syntactic information extracted from documents is more useful than that from queries. On the other hand, it has been demonstrated that by restricting dependencies to those corresponding to noun phrases, important reductions of storage and management costs can be achieved, albeit at the expense of a slight reduction in performance.Ministerio de Economía y Competitividad; FFI2014-51978-C2-1-RRede Galega de Procesamento da Linguaxe e Recuperación de Información; CN2014/034Ministerio de Economía y Competitividad; BES-2015-073768Ministerio de Economía y Competitividad; FFI2014-51978-C2-2-

Does the MS Spell Checker Effectively Correct Non-Native English Writers’ Errors? A Case Study of Saudi University Students

Those learning English as a second or foreign language use spell checkers to correct the mistakes and errors they may have made while typing texts on a computer However scholars have debated the effectiveness of such checkers which were originally designed to fix the spelling mistakes of native speakers An example of these checkers is the Microsoft MS Word program which constitutes the focus of the current study This study examined how MS Word treats misspellings made by Saudi learners of English as a foreign language It specifically addressed three research questions 1 which L2 spelling errors were successfully fixed by MS Word 2 which L2 spelling errors were unsuccessfully fixed by MS Word and 3 how did intermediate L2 learners respond to alternative corrections provided by MS Word A screentracking software Screencast-O-Matic was used to monitor the MS Word spell checker s treatment of misspelled words It was also used to track learners reactions to alternative corrections provided by MS Word in real time The study analysed 401 errors made by25 female intermediate-level English learners at a Saudi universit

Spell checkers and correctors : a unified treatment

The aim of this dissertation is to provide a unified treatment of various spell checkers and correctors. Firstly, the spell checking and correcting problems are formally described in mathematics in order to provide a better understanding of these tasks. An approach that is similar to the way in which denotational semantics used to describe programming languages is adopted. Secondly, the various attributes of existing spell checking and correcting techniques are discussed. Extensive studies on selected spell checking/correcting algorithms and packages are then performed. Lastly, an empirical investigation of various spell checking/correcting packages is presented. It provides a comparison and suggests a classification of these packages in terms of their functionalities, implementation strategies, and performance. The investigation was conducted on packages for spell checking and correcting in English as well as in Northern Sotho and Chinese. The classification provides a unified presentation of the strengths and weaknesses of the techniques studied in the research. The findings provide a better understanding of these techniques in order to assist in improving some existing spell checking/correcting applications and future spell checking/correcting package designs and implementations.Dissertation (MSc)--University of Pretoria, 2009.Computer Scienceunrestricte

Ask Language Model to Clean Your Noisy Translation Data

Transformer models have demonstrated remarkable performance in neural machine translation (NMT). However, their vulnerability to noisy input poses a significant challenge in practical implementation, where generating clean output from noisy input is crucial. The MTNT dataset is widely used as a benchmark for evaluating the robustness of NMT models against noisy input. Nevertheless, its utility is limited due to the presence of noise in both the source and target sentences. To address this limitation, we focus on cleaning the noise from the target sentences in MTNT, making it more suitable as a benchmark for noise evaluation. Leveraging the capabilities of large language models (LLMs), we observe their impressive abilities in noise removal. For example, they can remove emojis while considering their semantic meaning. Additionally, we show that LLM can effectively rephrase slang, jargon, and profanities. The resulting datasets, called C-MTNT, exhibit significantly less noise in the target sentences while preserving the semantic integrity of the original sentences. Our human and GPT-4 evaluations also lead to a consistent conclusion that LLM performs well on this task. Lastly, experiments on C-MTNT showcased its effectiveness in evaluating the robustness of NMT models, highlighting the potential of advanced language models for data cleaning and emphasizing C-MTNT as a valuable resource.Comment: EMNLP 2023, Finding