CTEMP: A Chinese Temporal Parser for Extracting and Normalizing Temporal Information

We introduce a rule based tagger of temporal expressions, ITUTime, for detecting and normalizing temporal expressions in Turkish language. The proposed system is morphologically aware and does not require any preprocessing steps, since ITUTime operates on free text. We also establish the first temporally annotated dataset of Turkish language. The work presented here locates itself as a baseline for tagging Turkish temporal expressions. The proposed system is evaluated on manually annotated test dataset and achieved 0.89 F1 score on recognizing and 0.89 F1 score on normalization.

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“…Chinese: CTemp [6] is well known temporal expression extraction and normalization system for Chinese, which is based on temporal grammar rules.…”

Section: Related Workmentioning

confidence: 99%

ITUTime: Turkish Temporal Expression Extraction and Normalization

Uzun

Tantuğ

2021

Lecture Notes in Networks and Systems

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“…As defined in [4], Chinese temporal expression can be classified as precise temporal expressions, fuzzy temporal expressions, modified temporal expressions, set-denoting temporal expressions, non-specific temporal expressions and so on. In [10], temporal parser for extracting and normalizing temporal expressions from Chinese texts is presented, the author also propose a temporal framework, which include basic temporal objects and relations, the measurement and classification of temporal expressions. For the purpose of inferring temporal information from multiple-clause sentences, a computational model based on machine learning and heterogeneous collaborative bootstrapping is build in [11] and the effects of linguistic features such as tense/aspect, temporal connectives, and discourse structures are also considered.…”

Section: Related Workmentioning

confidence: 99%

Extracting Event Temporal Information Based on Web

Yuan

Chen

Wang

et al. 2009

2009 Second International Symposium on Knowledge Acquisition and Modeling

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Temporal information is an important characteristic of event. It can be used in information retrieval process to organize the returned result. In Chinese, the presentations of time expression are very complex, which make it difficult to both accurately recognize a time expression and precisely connecting it with a given event in a web page that contains multiple events. To address these problems, this paper presents an innovative event time extraction model. Rather than just rely on local context within a web page or a text, this model applies global context provided by all the web pages that had been automatically judged as related with a given event. Our experiment results based on the evaluation criterions show the feasibility of the provided model.

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“…According to the TimeML standard, a label needs to contain four attributes: (1) tid, the index of temporal information in the record; (2) Type, the temporal type; (3) Value, the normalized date of a TE; and (4) anchorTimeID, the index of the reference time of the current TE. Each TE has a unique tag (eg, <TIMEX3 tid="t1" Type="TIME" Value="2014-10-11T07:48:16">2014/10/11 7:48:16</TIMEX3>, <TIMEX3 tid="t7" Type="DATE" Value="2014-08-02" anchorTimeID="t2">1 周前</TIMEX3>, <TIMEX3 tid="t4" Type="DURATION" Value="P1Y"> 1年余</TIMEX3>, <TIMEX3 tid="t13" Type="SET" Value=" [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]" anchorTimeID="t11"> 第7、9、14 天 </TIMEX3>). These 900 EMRs contain 12,096 TEs (13.44…”

Section: Datasetsmentioning

confidence: 99%

“…Meanwhile, research related to Chinese TE extraction and normalization was reported. Wu et al [26] proposed a temporal parser to extract and standardize Chinese TEs. Zhou et al [27] established a framework concentrating on processing narrative clinical records in Chinese, including a regular expression matching-based method for TE identification and an approach for temporal relationship extraction using CRF.…”

Section: Introductionmentioning

confidence: 99%

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

Pan¹,

Chen²,

Weng³

et al. 2019

Preprint

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BACKGROUND Temporal information frequently exists in the representation of the disease progress, prescription, medication, the 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 so as to promote the clinical research and practice. OBJECTIVE The study is 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 expressions 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 1,459 discharge summaries of a domestic Grade-A Class-three hospital. The results present 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 summaries and demonstrated its effectiveness on temporal expression normalization with experiment results.

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CTEMP: A Chinese Temporal Parser for Extracting and Normalizing Temporal Information

Cited by 20 publications

References 10 publications

ITUTime: Turkish Temporal Expression Extraction and Normalization

ITUTime: Turkish Temporal Expression Extraction and Normalization

Extracting Event Temporal Information Based on Web

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

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