Extraction Of Temporal Expressions Research Articles

Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts

Background and ObjectiveWith the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of events in order of time. Temporal information runs through the whole process of beginning, proceeding, and ending of events, and plays an important role in many natural language processing applications, such as information extraction, question answering, and text summary. Accurately extracting temporal information from Chinese texts and automatically mapping the temporal expressions in natural language to the time axis are crucial to understanding the development of events and dynamic changes in them. MethodsThis study proposes a method integrating machine learning with linguistic features (IMLLF) for extraction and normalization of temporal expressions in Chinese texts to achieve the above objectives. Linguistic features are constructed by analyzing the expression rules of temporal information, and are combined with machine learning to map the natural language form of time onto a one-dimensional timeline. The web text dataset we build is divided into five parts for five-fold cross-validation, to compare the influence of different combinations of linguistic features and different methods. In the open medical dialog dataset, based on the training model obtained from the web text dataset, 200 disease descriptions are randomly selected each time for three rounds of experiments. ResultsThe F1 of multi-feature fusion is 95.2%, which is better than the single-feature and double-feature combination. The results of experiments showed that the proposed IMLLF method can improve the accuracy of recognition of temporal information in Chinese to a greater extent than classical methods, with an F1-score of over 95% on the web text dataset and medical conversation dataset. In terms of the normalization of time expressions, the accuracy of the IMLLF method is higher than 93%. ConclusionsIMLLF has better results in extracting and normalizing time expressions on the web text dataset and the medical conversation dataset, which verifies the universality of IMLLF to identify and quantify temporal information. IMLLF method can accurately map the time information to the time axis, which is convenient for doctors to intuitively see when and what happened to the patient, and helps to make better medical decisions.

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

BackgroundTemporal 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.ObjectiveThe goal of the study was to propose a novel approach for extracting and normalizing temporal expressions from Chinese narrative clinical text.MethodsTNorm, 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.ResultsThe 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.ConclusionsThis 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.

TempCourt: evaluation of temporal taggers on a new corpus of court decisions

AbstractThe extraction and processing of temporal expressions (TEs) in textual documents have been extensively studied in several domains; however, for the legal domain it remains an open challenge. This is possibly due to the scarcity of corpora in the domain and the particularities found in legal documents that are highlighted in this paper. Considering the pivotal role played by temporal information when it comes to analyzing legal cases, this paper presents TempCourt, a corpus of 30 legal documents from the European Court of Human Rights, the European Court of Justice, and the United States Supreme Court with manually annotated TEs. The corpus contains two different temporal annotation sets that adhere to the TimeML standard, the first one capturing all TEs and the second dedicated to TEs that are relevant for the case under judgment (thus excluding dates of previous court decisions). The proposed gold standards are subsequently used to compare ten state-of-the-art cross-domain temporal taggers, and to identify not only the limitations of cross-domain temporal taggers but also limitations of the TimeML standard when applied to legal documents. Finally, the paper identifies the need for dedicated resources and the adaptation of existing tools, and specific annotation guidelines that can be adapted to different types of legal documents.

A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts

BackgroundTemporal expression extraction and normalization is a fundamental and essential step in clinical text processing and analyzing. Though a variety of commonly used NLP tools are available for medical temporal information extraction, few work is satisfactory for multi-lingual heterogeneous clinical texts.MethodsA novel method called TEER is proposed for both multi-lingual temporal expression extraction and normalization from various types of narrative clinical texts including clinical data requests, clinical notes, and clinical trial summaries. TEER is characterized as temporal feature summarization, heuristic rule generation, and automatic pattern learning. By representing a temporal expression as a triple <M, A, N>, TEER identifies temporal mentions M, assigns type attributes A to M, and normalizes the values of M into formal representations N.ResultsBased on two heterogeneous clinical text datasets: 400 actual clinical requests in English and 1459 clinical discharge summaries in Chinese. TEER was compared with six state-of-the-art baselines. The results showed that TEER achieved a precision of 0.948 and a recall of 0.877 on the English clinical requests, while a precision of 0.941 and a recall of 0.932 on the Chinese discharge summaries.ConclusionsAn automated method TEER for multi-lingual temporal expression extraction was presented. Based on the two datasets containing heterogeneous clinical texts, the comparison results demonstrated the effectiveness of the TEER method in multi-lingual temporal expression extraction from heterogeneous narrative clinical texts.

Mining medical related temporal information from patients’ self-description

Purpose The purpose of this paper is to develop a new method to extract medical temporal information from online health communities. Design/methodology/approach The authors trained a conditional random-filed model for the extraction of temporal expressions. The temporal relation identification is considered as a classification task and several support vector machine classifiers are built in the proposed method. For the model training, the authors extracted some high-level semantic features including co-reference relationship of medical concepts and the semantic similarity among words. Findings For the extraction of TIMEX, the authors find that well-formatted expressions are easy to recognize, and the main challenge is the relative TIMEX such as “three days after onset”. It also shows the same difficulty for normalization of absolute date or well-formatted duration, whereas frequency is easier to be normalized. For the identification of DocTimeRel, the result is fairly well, and the relation is difficult to identify when it involves a relative TIMEX or a hypothetical concept. Originality/value The authors proposed a new method to extract temporal information from the online clinical data and evaluated the usefulness of different level of syntactic features in this task.

A temporal model in Electronic Health Record search

Electronic Health Records (EHRs) refer to a collection of patient data, including diagnosis, medical history, medication, allergies, etc., mostly contained in the form of unstructured text. EHRs are designed to capture the state of a patient over time, thus the temporal information is crucial. Most previous works processing time in EHRs narrative focused on temporal expression extraction, using textual dimension to embody the temporal dimension. In this paper, we propose to model the textual and the temporal dimension of EHRs narrative jointly. To meet the challenge, we propose to model the EHRs narrative as temporal sequential data. A novel representation framework is designed to model the clinical narrative text as document sequence, where the textual and temporal dimension are modeled simultaneously. In the framework, a dynamic time warping based measure is proposed to quantify the similarity between EHRs of different patients. To verify the effectiveness of the model, the proposed model is applied in EHRs search via clustering algorithm. Experiments on real-world EHRs data set demonstrate that the proposed model sufficiently expresses the temporal feature of the EHRs and provides an effective solution for measuring the temporal similarity between EHRs of different patients.

Temporal expression extraction with extensive feature type selection and a posteriori label adjustment

The automatic extraction of temporal information from written texts is pivotal for many Natural Language Processing applications such as question answering, text summarisation and information retrieval. It allows to filter information and infer temporal flows of events.This paper presents ManTIME, a general domain temporal expression identification and normalisation system, and systematically explores the impact of different features and training corpora on the performance. The identification phase combines the use of conditional random fields along with a post-processing pipeline, whereas the normalisation phase is carried out using NorMA, an open-source rule-based temporal normaliser.We investigate the performance variation with respect to different feature types. Specifically, we show that the use of WordNet-based features in the identification task negatively affects the overall performance, and that there is no statistically significant difference in the results based on gazetteers, shallow parsing and propositional noun phrases labels on top of the morpho-lexical features. We also show that the use of silver data (alone or in addition to the human-annotated ones) does not improve the performance.We evaluate six combinations of training data and post-processing pipeline with respect to the TempEval-3 benchmark test set. The best run achieved 0.95 (precision), 0.85 (recall) and 0.90 (Fβ=1) in the identification phase. Normalisation accuracies are 0.86 (for type attribute) and 0.77 (for value attribute).The proposed approach ranked 3rd in the TempEval-3 challenge (task A) as the best performing machine learning-based system among 21 participants.

Extraction of events and temporal expressions from clinical narratives

This paper addresses an important task of event and timex extraction from clinical narratives in context of the i2b2 2012 challenge. State-of-the-art approaches for event extraction use a multi-class classifier for finding the event types. However, such approaches consider each event in isolation. In this paper, we present a sentence-level inference strategy which enforces consistency constraints on attributes of those events which appear close to one another. Our approach is general and can be used for other tasks as well. We also design novel features like clinical descriptors (from medical ontologies) which encode a lot of useful information about the concepts. For timex extraction, we adapt a state-of-the-art system, HeidelTime, for use in clinical narratives and also develop several rules which complement HeidelTime. We also give a robust algorithm for date extraction. For the event extraction task, we achieved an overall F1 score of 0.71 for determining span of the events along with their attributes. For the timex extraction task, we achieved an F1 score of 0.79 for determining span of the temporal expressions. We present detailed error analysis of our system and also point out some factors which can help to improve its accuracy.

A hybrid system for temporal information extraction from clinical text

To develop a comprehensive temporal information extraction system that can identify events, temporal expressions, and their temporal relations in clinical text. This project was part of the 2012 i2b2 clinical natural language processing (NLP) challenge on temporal information extraction. The 2012 i2b2 NLP challenge organizers manually annotated 310 clinic notes according to a defined annotation guideline: a training set of 190 notes and a test set of 120 notes. All participating systems were developed on the training set and evaluated on the test set. Our system consists of three modules: event extraction, temporal expression extraction, and temporal relation (also called Temporal Link, or 'TLink') extraction. The TLink extraction module contains three individual classifiers for TLinks: (1) between events and section times, (2) within a sentence, and (3) across different sentences. The performance of our system was evaluated using scripts provided by the i2b2 organizers. Primary measures were micro-averaged Precision, Recall, and F-measure. Our system was among the top ranked. It achieved F-measures of 0.8659 for temporal expression extraction (ranked fourth), 0.6278 for end-to-end TLink track (ranked first), and 0.6932 for TLink-only track (ranked first) in the challenge. We subsequently investigated different strategies for TLink extraction, and were able to marginally improve performance with an F-measure of 0.6943 for TLink-only track.

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.

The Extraction of Chinese Temporal Expressions Based on LEX

Temporal Expressions are important structures in natural language. Temporal information is useful in many NLP applications, such as information extraction, question answering and summarization. In this paper, we present an approach for extracting temporal expressions from Chinese texts. Using LEX parser, defining grammar rules of temporal expressions as LEX source program through the cooperation of LEX and C compiler, get the temporal expressions from unprocessed Chinese corpus. Our experiments demonstrate that on the TempEval 2010 Chinese corpus this approach is valid with the F1-measure values of 93.97%.

Temporal Information Processing: A Survey

Temporal Information Processing is a subfield of Natural Language Processing, valuable in many tasks like Question Answering and Summarization. Temporal Information Processing is broadened, ranging from classical theories of time and language to current computational approaches for Temporal Information Extraction. This later trend consists on the automatic extraction of events and temporal expressions. Such issues have attracted great attention especially with the development of annotated corpora and annotations schemes mainly TimeBank and TimeML. In this paper, we give a survey of Temporal Information Extraction from Natural Language texts.

Automatic system for identifying and categorizing temporal relations in natural language

Nowadays, the automatic processing of digitalized documents is crucial to cope with the increasing amount of information available. This issue is addressed from the natural language processing (NLP) research field. One of the tasks required for many NLP applications is temporal information processing. It involves the automatic extraction and interpretation of temporal expressions, events, and their relations. Specifically, the identification and the categorization of temporal relations are the most complex subtasks yet to solve, judging from the results reported in the latest international evaluation exercise. Temporal relation identification has been addressed by very few approaches, and the current categorization approaches are still not a definitive solution. This paper presents a system that approaches temporal relation identification and categorization. The former is approached with a knowledge-driven strategy and the later with data-driven strategy based on different machine-learning techniques. Our proposal has been empirically evaluated over the currently available English data sets annotated with temporal information (TimeBank and AQUAINT) in a 10-fold cross-validated experiment. The results obtained support that the presented approach achieves a high performance. It improves the baseline F1 by 46% and outperforms the state of the art. © 2012 Wiley Periodicals, Inc.

Multilingual and cross-domain temporal tagging

Extraction and normalization of temporal expressions from documents are important steps towards deep text understanding and a prerequisite for many NLP tasks such as information extraction, question answering, and document summarization. There are different ways to express (the same) temporal information in documents. However, after identifying temporal expressions, they can be normalized according to some standard format. This allows the usage of temporal information in a term- and language-independent way. In this paper, we describe the challenges of temporal tagging in different domains, give an overview of existing annotated corpora, and survey existing approaches for temporal tagging. Finally, we present our publicly available temporal tagger HeidelTime, which is easily extensible to further languages due to its strict separation of source code and language resources like patterns and rules. We present a broad evaluation on multiple languages and domains on existing corpora as well as on a newly created corpus for a language/domain combination for which no annotated corpus has been available so far.

An Overview of Temporal Information Extraction

Research of temporal Information Extraction was regarded as a subtask of named entity recognition in 1990's. To date, the scope of this research is broadened, ranging from temporal expression extraction and annotation to temporal reasoning and understanding. This area of research is now a hot NLP topic and the results are applicable to question answering, information extraction, text summarization, etc. This paper presents the past, present and future research development in temporal information extraction.