SystemT: Declarative Text Understanding for Enterprise

Chiticariu, Laura; Danilevsky, Marina; Li, Yunyao; Reiss, Frederick; Zhu, Huaiyu

doi:10.18653/v1/n18-3010

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…The core of the NLP extraction pipeline transforms textual patterns between ontological annotations into sets of RDF triples, which constitute a partial rule knowledge graph; it uses two domain-agnostic approaches: semantic role labeling 25 and semantic reasoning 24 . Semantic role labeling is based on a declarative information extraction system 58 that can identify actions and roles in a sentence (who is the agent, what is the theme or context of the action, if there are any conditionals, the polarity of the action, temporal information, etc.). We use the ontology definitions for the domain and range of properties to reason over the semantic roles, and to identify semantically compatible relation and entity/value pairs in a sentence.…”

Section: Extraction Of Rules From Textmentioning

confidence: 99%

Collaborative artificial intelligence system for investigation of healthcare claims compliance

Sbodio,

López,

Hoang

et al. 2024

Sci Rep

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Healthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labelled data and possibly lacking interpretability. We present Clais, a collaborative artificial intelligence system for claims analysis. Clais automatically extracts human-interpretable rules from healthcare policy documents (0.72 F1-score), and it enables professionals to edit and validate the extracted rules through an intuitive user interface. Clais executes the rules on claim records to identify non-compliance: on this task Clais significantly outperforms two baseline machine learning models, and its median F1-score is 1.0 (IQR = 0.83 to 1.0) when executing the extracted rules, and 1.0 (IQR = 1.0 to 1.0) when executing the same rules after human curation. Professionals confirm through a user study the usefulness of Clais in making their workflow simpler and more effective.

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Section: Extraction Of Rules From Textmentioning

confidence: 99%

Collaborative artificial intelligence system for investigation of healthcare claims compliance

Sbodio,

López,

Hoang

et al. 2024

Sci Rep

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“…Braghin S et al (2019) [ 15 ], proposed a hybrid framework for the de-identification of health information for privacy protection. Their framework combines different annotators such as PRIMA Annotator [ 6 ], Apache OpenNLP, Stanford CoreNLP [ 65 ], SystemT [ 18 ], Advanced Care Insights, Extensibility, and Integration to maximize performance and increase generalization. Additionally, Jian Z et al (2017) [ 47 ] proposed a model for the de-identification of Chinese clinical text with less annotation effort using a cascaded method that combines rule-based and ML methods to identify Patient Health Information (PHI) and replace them with a corresponding safe surrogate in sentences with PHI.…”

Section: Challenges Identified and Scope Of Work In Healthcare Using ...mentioning

confidence: 99%

Optimizing healthcare system by amalgamation of text processing and deep learning: a systematic review

Rani

Jain

2023

Multimed Tools Appl

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The explosion of clinical textual data has drawn the attention of researchers. Owing to the abundance of clinical data, it is becoming difficult for healthcare professionals to take real-time measures. The tools and methods are lacking when compared to the amount of clinical data generated every day. This review aims to survey the text processing pipeline with deep learning methods such as CNN, RNN, LSTM, and GRU in the healthcare domain and discuss various applications such as clinical concept detection and extraction, medically aware dialogue systems, sentiment analysis of drug reviews shared online, clinical trial matching, and pharmacovigilance. In addition, we highlighted the major challenges in deploying text processing with deep learning to clinical textual data and identified the scope of research in this domain. Furthermore, we have discussed various resources that can be used in the future to optimize the healthcare domain by amalgamating text processing and deep learning.

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“…Information extraction has been addressed with different methods and techniques, including (i) only Conditional Random Fields (CRFs) models (FINKEL; GRENAGER; MANNING, 2005); (ii) character-level embeddings instead of whole words (LAMPLE et al, 2016), and (iii) rule-based approaches (CHITICARIU et al, 2018). Yet, recent studies (YADAV; BETHAR, 2018) show the benefits of ML-based approaches such as recurrent neural networks.…”

Section: Natural Language Processingmentioning

confidence: 99%

Enabling Self-Driving Networks with Machine Learning

Jacobs

Ferreira

Granville

2023

NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium

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As modern networks grow in size and complexity, they also become increasingly prone to human errors. This trend has driven both industry and academia to try to automate management and control tasks, aiming to reduce human interaction with the network and human-made mistakes. Ideally, researchers envision a network design that is not only automatic (i.e., dependent of human instructions) but autonomous (i.e., capable of making its own decisions). Autonomous networking has been a goal sought for years, with many different concepts, designs and implementations, but it was never fully realized, mainly due to technological limitations. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) introduced a breath of fresh air into this concept, reemerging as the re-branded concept of self-driving networks, in view of its autonomous car counterparts.In broad terms, a self-driving network is an autonomous network capable of acting according to high-level intents from an operator and automatically adapting to changes in traffic and user behavior. To achieve that vision, a network would need to fulfill four major requirements: (i) understand high-level intents from an operator to dictate its behavior, (ii) monitor itself based on input intents, (iii) predict and identify patterns from monitored data and (iv) adapt itself to new behaviors without the intervention of an operator.As fulfilling the requirements of a self-driving network requires heavily relying on ML models to make decisions and classifications that directly impact the network, one particular issue becomes prominent with this design: trust. Applying ML to solve networking management tasks, such as the ones described above, has been a popular trend among researchers recently. However, despite the topic receiving much attention, industry operators have been reluctant to take advantage of such solutions, mainly because of the black-box nature of ML models which produce decisions without any explanation or reason as to which those decisions were made. Given the high-stakes nature of production networks, it becomes impossible to trust a ML model that may take system-breaking actions automatically, and most important to the scope of this thesis, a prohibitive challenge that must be addressed if a self-driving network design is ever to be achieved.The present thesis aims to enable self-driving networks by tackling the problem of the inherent lack of trust in ML models that empower it. To that end, we assess and scrutinize the decision-making process of ML-based classifiers used to compose a self-driving network. First, we investigate and evaluate the accuracy and credibility of classifications made by ML models used to process high-level intents from the operator. For that eval-uation, we propose a novel conversational interface called LUMI that allows operators to use natural language to describe how the network should behave. Second, we analyze and assess the accuracy and credibility of ML models' decisions to self-configure the network according to...

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SystemT: Declarative Text Understanding for Enterprise

Cited by 10 publications

References 15 publications

Collaborative artificial intelligence system for investigation of healthcare claims compliance

Collaborative artificial intelligence system for investigation of healthcare claims compliance

Optimizing healthcare system by amalgamation of text processing and deep learning: a systematic review

Enabling Self-Driving Networks with Machine Learning

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