Recognition Of Temporal Expressions Research Articles

Recognition and normalisation of temporal expressions using conditional random fields and cascade of partial rules

Abstract This article introduces the issue of recognition and normalisation of temporal expressions for the Polish language. We describe what temporal information is and we present TimeML specification, adapted to Polish as a model for the description of temporal expressions. Classes of temporal expressions are presented as well as guidelines for annotation, normalisation of these expressions and our approach to corpus annotation and temporal expressions recognition. The key aspect of the work is the description of the features used for the recognition, the use of the method for selection and creation of feature templates for the model of Conditional Random Fields. We demonstrate the experiments and conclusions drawn from them.

Recognition and Normalization of Temporal Expressions in Serbian Medical Narratives

Recognition and Normalization of Temporal Expressions in Serbian Medical Narratives

The analysis and recognition of Chinese temporal expressions based on a mixtured model using statistics and rules

As the first step of temporal information understanding, the results of temporal expressions recognition will directly affect further usage of temporal information. For Chinese language, there are many distinct characters both in word morphology and syntax in temporal expressions compared with the Western languages. Classifications and constructions of Chinese temporal expressions were analysed, and an approach for extracting temporal expressions from Chinese texts was presented in this paper. The model comprises of a cascade of rule-based and machine-learning pattern recognition procedures. Conditional random fields (CRFs) were applied to recognise time units rather than time expressions to avoid the boundary localisation problems in Chinese temporal expressions. Rules for the temporal expressions boundary localisation were formulated based on time triggers thesaurus and time affix words thesaurus. The F-measure of temporal expressions identification was 95.93% on the temporal 2010 Chinese corpus. The experiments result showed the validity of the proposed approach.

The analysis and recognition of Chinese temporal expressions based on a mixtured model using statistics and rules

The analysis and recognition of Chinese temporal expressions based on a mixtured model using statistics and rules

A flexible framework for recognizing events, temporal expressions, and temporal relations in clinical text

To provide a natural language processing method for the automatic recognition of events, temporal expressions, and temporal relations in clinical records. A combination of supervised, unsupervised, and rule-based methods were used. Supervised methods include conditional random fields and support vector machines. A flexible automated feature selection technique was used to select the best subset of features for each supervised task. Unsupervised methods include Brown clustering on several corpora, which result in our method being considered semisupervised. On the 2012 Informatics for Integrating Biology and the Bedside (i2b2) shared task data, we achieved an overall event F1-measure of 0.8045, an overall temporal expression F1-measure of 0.6154, an overall temporal link detection F1-measure of 0.5594, and an end-to-end temporal link detection F1-measure of 0.5258. The most competitive system was our event recognition method, which ranked third out of the 14 participants in the event task. Analysis reveals the event recognition method has difficulty determining which modifiers to include/exclude in the event span. The temporal expression recognition method requires significantly more normalization rules, although many of these rules apply only to a small number of cases. Finally, the temporal relation recognition method requires more advanced medical knowledge and could be improved by separating the single discourse relation classifier into multiple, more targeted component classifiers. Recognizing events and temporal expressions can be achieved accurately by combining supervised and unsupervised methods, even when only minimal medical knowledge is available. Temporal normalization and temporal relation recognition, however, are far more dependent on the modeling of medical knowledge.

Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives

Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we developed and evaluated a system to automatically extract temporal expressions and events from clinical narratives. The extracted temporal expressions were additionally normalized by assigning type, value, and modifier. The system combines rule-based and machine learning approaches that rely on morphological, lexical, syntactic, semantic, and domain-specific features. Rule-based components were designed to handle the recognition and normalization of temporal expressions, while conditional random fields models were trained for event and temporal recognition. The system achieved micro F scores of 90% for the extraction of temporal expressions and 87% for clinical event extraction. The normalization component for temporal expressions achieved accuracies of 84.73% (expression's type), 70.44% (value), and 82.75% (modifier). Compared to the initial agreement between human annotators (87-89%), the system provided comparable performance for both event and temporal expression mining. While (lenient) identification of such mentions is achievable, finding the exact boundaries proved challenging. The system provides a state-of-the-art method that can be used to support automated identification of mentions of clinical events and temporal expressions in narratives either to support the manual review process or as a part of a large-scale processing of electronic health databases.

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

Combining automatic acquisition of knowledge with machine learning approaches for multilingual temporal recognition and normalization

This paper presents an improvement in the temporal expression (TE) recognition phase of a knowledge based system at a multilingual level. For this purpose, the combination of different approaches applied to the recognition of temporal expressions are studied. In this work, for the recognition task, a knowledge based system that recognizes temporal expressions and had been automatically extended to other languages (TERSEO system) was combined with a system that recognizes temporal expressions using machine learning techniques. In particular, two different techniques were applied: maximum entropy model (ME) and hidden Markov model (HMM), using two different types of tagging of the training corpus: (1) BIO model tagging of literal temporal expressions and (2) BIO model tagging of simple patterns of temporal expressions. Each system was first evaluated independently and then combined in order to: (a) analyze if the combination gives better results without increasing the number of erroneous expressions in the same percentage and (b) decide which machine learning approach performs this task better. When the TERSEO system is combined with the maximum entropy approach the best results for F-measure (89%) are obtained, improving TERSEO recognition by 4.5 points and ME recognition by 7.