ParsTime: Rule-Based Extraction and Normalization of Persian Temporal Expressions

2010 ◽

pp. 259-277 ◽

Cited By ~ 1

Author(s):

David Lo ◽

Siau-Cheng Khoo ◽

Chao Liu

Keyword(s):

Instant Messaging ◽

Program Execution ◽

Temporal Expression ◽

Rule Based ◽

Specification Mining ◽

Past Time ◽

Execution Traces ◽

Sequence Of Events ◽

Temporal Expressions ◽

Mining Tools

Specification mining is a process of extracting specifications, often from program execution traces. These specifications can in turn be used to aid program understanding, monitoring and verification. There are a number of dynamic-analysis-based specification mining tools in the literature, however none so far extract past time temporal expressions in the form of rules stating: “whenever a series of events occur, previously another series of events happened before”. Rules of this format are commonly found in practice and useful for various purposes. Most rule-based specification mining tools only mine future-time temporal expression. Many past-time temporal rules like “whenever a resource is used, it was allocated before” are asymmetric as the other direction does not holds. Hence, there is a need to mine past-time temporal rules. In this chapter, the authors describe an approach to mine significant rules of the above format occurring above a certain statistical thresholds from program execution traces. The approach start from a set of traces, each being a sequence of events (i.e., method invocations) and resulting in a set of significant rules obeying minimum thresholds of support and confidence. A rule compaction mechanism is employed to reduce the number of reported rules significantly. Experiments on traces of JBoss Application Server and Jeti instant messaging application shows the utility of our approach in inferring interesting past-time temporal rules.

Download Full-text

Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach

JMIR Medical Informatics ◽

10.2196/17652 ◽

2020 ◽

Vol 8 (7) ◽

pp. e17652

Author(s):

Xiaoyi Pan ◽

Boyu Chen ◽

Heng Weng ◽

Yongyi Gong ◽

Yingying Qu

Keyword(s):

Prescription Medication ◽

Discharge Summary ◽

Machine Learning Algorithms ◽

Pattern Learning ◽

Temporal Expression ◽

Rule Based ◽

Clinical Text ◽

Novel Approach ◽

Temporal Expressions ◽

Expression Classification

Background Temporal information frequently exists in the representation of the disease progress, prescription, medication, surgery progress, or discharge summary in narrative clinical text. The accurate extraction and normalization of temporal expressions can positively boost the analysis and understanding of narrative clinical texts to promote clinical research and practice. Objective The goal of the study was to propose a novel approach for extracting and normalizing temporal expressions from Chinese narrative clinical text. Methods TNorm, a rule-based and pattern learning-based approach, has been developed for automatic temporal expression extraction and normalization from unstructured Chinese clinical text data. TNorm consists of three stages: extraction, classification, and normalization. It applies a set of heuristic rules and automatically generated patterns for temporal expression identification and extraction of clinical texts. Then, it collects the features of extracted temporal expressions for temporal type prediction and classification by using machine learning algorithms. Finally, the features are combined with the rule-based and a pattern learning-based approach to normalize the extracted temporal expressions. Results The evaluation dataset is a set of narrative clinical texts in Chinese containing 1459 discharge summaries of a domestic Grade A Class 3 hospital. The results show that TNorm, combined with temporal expressions extraction and temporal types prediction, achieves a precision of 0.8491, a recall of 0.8328, and a F1 score of 0.8409 in temporal expressions normalization. Conclusions This study illustrates an automatic approach, TNorm, that extracts and normalizes temporal expression from Chinese narrative clinical texts. TNorm was evaluated on the basis of discharge summary data, and results demonstrate its effectiveness on temporal expression normalization.

Download Full-text

Normalization of relative and incomplete temporal expressions in clinical narratives

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocu004 ◽

2015 ◽

Vol 22 (5) ◽

pp. 1001-1008 ◽

Cited By ~ 7

Author(s):

Weiyi Sun ◽

Anna Rumshisky ◽

Ozlem Uzuner

Keyword(s):

Error Analysis ◽

Anchor Point ◽

System Component ◽

Classification Problems ◽

Rule Based ◽

Feature Sets ◽

Clinical Narrative ◽

Temporal Expressions ◽

Relation Classification ◽

Matching Criteria

Abstract Objective To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives. Methods We analyzed the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothesis. We annotated the RI-TIMEXes in three corpora to study the characteristics of RI-TMEXes in different domains. This informed the design of our RI-TIMEX normalization system for the clinical domain, which consists of an anchor point classifier, an anchor relation classifier, and a rule-based RI-TIMEX text span parser. We experimented with different feature sets and performed an error analysis for each system component. Results The annotation confirmed the hypotheses that we can simplify the RI-TIMEXes normalization task using two multi-label classifiers. Our system achieves anchor point classification, anchor relation classification, and rule-based parsing accuracy of 74.68%, 87.71%, and 57.2% (82.09% under relaxed matching criteria), respectively, on the held-out test set of the 2012 i2b2 temporal relation challenge. Discussion Experiments with feature sets reveal some interesting findings, such as: the verbal tense feature does not inform the anchor relation classification in clinical narratives as much as the tokens near the RI-TIMEX. Error analysis showed that underrepresented anchor point and anchor relation classes are difficult to detect. Conclusions We formulate the RI-TIMEX normalization problem as a pair of multi-label classification problems. Considering only RI-TIMEX extraction and normalization, the system achieves statistically significant improvement over the RI-TIMEX results of the best systems in the 2012 i2b2 challenge.

Download Full-text

Temporal Expression Classification and Normalization From Chinese Narrative Clinical Texts: Pattern Learning Approach (Preprint)

10.2196/preprints.17652 ◽

2019 ◽

Author(s):

Xiaoyi Pan ◽

Boyu Chen ◽

Heng Weng ◽

Yongyi Gong ◽

Yingying Qu

Keyword(s):

Prescription Medication ◽

Discharge Summary ◽

Machine Learning Algorithms ◽

Pattern Learning ◽

Temporal Expression ◽

Rule Based ◽

Clinical Text ◽

Novel Approach ◽

Temporal Expressions ◽

Expression Classification

BACKGROUND Temporal information frequently exists in the representation of the disease progress, prescription, medication, surgery progress, or discharge summary in narrative clinical text. The accurate extraction and normalization of temporal expressions can positively boost the analysis and understanding of narrative clinical texts to promote clinical research and practice. OBJECTIVE The goal of the study was to propose a novel approach for extracting and normalizing temporal expressions from Chinese narrative clinical text. METHODS TNorm, a rule-based and pattern learning-based approach, has been developed for automatic temporal expression extraction and normalization from unstructured Chinese clinical text data. TNorm consists of three stages: extraction, classification, and normalization. It applies a set of heuristic rules and automatically generated patterns for temporal expression identification and extraction of clinical texts. Then, it collects the features of extracted temporal expressions for temporal type prediction and classification by using machine learning algorithms. Finally, the features are combined with the rule-based and a pattern learning-based approach to normalize the extracted temporal expressions. RESULTS The evaluation dataset is a set of narrative clinical texts in Chinese containing 1459 discharge summaries of a domestic Grade A Class 3 hospital. The results show that TNorm, combined with temporal expressions extraction and temporal types prediction, achieves a precision of 0.8491, a recall of 0.8328, and a F1 score of 0.8409 in temporal expressions normalization. CONCLUSIONS This study illustrates an automatic approach, TNorm, that extracts and normalizes temporal expression from Chinese narrative clinical texts. TNorm was evaluated on the basis of discharge summary data, and results demonstrate its effectiveness on temporal expression normalization.

Download Full-text

Using Conventional Articulation Tests With Highly Unintelligible Children

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.2301.52 ◽

1992 ◽

Vol 23 (1) ◽

pp. 52-60 ◽

Cited By ~ 1

Author(s):

Pamela G. Garn-Nunn ◽

Vicki Martin

Keyword(s):

Test Results ◽

Phonological Processes ◽

Rule Based ◽

Severity Level ◽

Error Sensitivity ◽

Conventional Tests ◽

Severity Levels ◽

Conventional Test ◽

Impaired Children

This study explored whether or not standard administration and scoring of conventional articulation tests accurately identified children as phonologically disordered and whether or not information from these tests established severity level and programming needs. Results of standard scoring procedures from the Assessment of Phonological Processes-Revised, the Goldman-Fristoe Test of Articulation, the Photo Articulation Test, and the Weiss Comprehensive Articulation Test were compared for 20 phonologically impaired children. All tests identified the children as phonologically delayed/disordered, but the conventional tests failed to clearly and consistently differentiate varying severity levels. Conventional test results also showed limitations in error sensitivity, ease of computation for scoring procedures, and implications for remediation programming. The use of some type of rule-based analysis for phonologically impaired children is highly recommended.

Download Full-text

Haunted by a Doppelgänger

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000221 ◽

2014 ◽

Vol 61 (1) ◽

pp. 12-22 ◽

Cited By ~ 11

Author(s):

Bettina von Helversen ◽

Stefan M. Herzog ◽

Jörg Rieskamp

Keyword(s):

Relevant Information ◽

Psychological Research ◽

Decision Makers ◽

Human Judgment ◽

Rule Based ◽

New Employees ◽

High Performing ◽

Facial Similarity ◽

Similarity Effect ◽

Judgment Strategy

Judging other people is a common and important task. Every day professionals make decisions that affect the lives of other people when they diagnose medical conditions, grant parole, or hire new employees. To prevent discrimination, professional standards require that decision makers render accurate and unbiased judgments solely based on relevant information. Facial similarity to previously encountered persons can be a potential source of bias. Psychological research suggests that people only rely on similarity-based judgment strategies if the provided information does not allow them to make accurate rule-based judgments. Our study shows, however, that facial similarity to previously encountered persons influences judgment even in situations in which relevant information is available for making accurate rule-based judgments and where similarity is irrelevant for the task and relying on similarity is detrimental. In two experiments in an employment context we show that applicants who looked similar to high-performing former employees were judged as more suitable than applicants who looked similar to low-performing former employees. This similarity effect was found despite the fact that the participants used the relevant résumé information about the applicants by following a rule-based judgment strategy. These findings suggest that similarity-based and rule-based processes simultaneously underlie human judgment.

Download Full-text