Temporal Information Extraction from Korean Texts

Jeong, Young-Seob; Kim, Zae Myung; Do, Hyun-Woo; Lim, Chae-Gyun; Choi, Ho-Jin

doi:10.18653/v1/k15-1028

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…Note that this is the information extraction task in the NLP field. We implemented this information extraction process using a publicly available library ‘slotminer’ ( ) that was originally designed for rule-based temporal information extraction [ 35 ]. We designed rules to detect and normalize the desired text patterns using the ‘slotminer’ library.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Postoperative Complications for Patients of End Stage Renal Disease

Jeong

Kim

et al. 2021

Sensors

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End stage renal disease (ESRD) is the last stage of chronic kidney disease that requires dialysis or a kidney transplant to survive. Many studies reported a higher risk of mortality in ESRD patients compared with patients without ESRD. In this paper, we develop a model to predict postoperative complications, major cardiac event, for patients who underwent any type of surgery. We compare several widely-used machine learning models through experiments with our collected data yellow of size 3220, and achieved F1 score of 0.797 with the random forest model. Based on experimental results, we found that features related to operation (e.g., anesthesia time, operation time, crystal, and colloid) have the biggest impact on model performance, and also found the best combination of features. We believe that this study will allow physicians to provide more appropriate therapy to the ESRD patients by providing information on potential postoperative complications.

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Section: Methodsmentioning

confidence: 99%

Prediction of Postoperative Complications for Patients of End Stage Renal Disease

Jeong

Kim

et al. 2021

Sensors

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“…Multiple versions of the TempEval corpus have also been created in different languages. Currently available languages are Arabic [73], French [88], Portuguese [89], Korean [90], Spanish [91], Romanian [92] and Italian [93]. All of the mentioned datasets are compared in Table 5.…”

Section: Temporal Extraction For Non-english Languagesmentioning

confidence: 99%

“…All of the mentioned datasets are compared in Table 5. English, Arabic, Chinese 750,000 FR-TB [88] French 61,000 Korean TB [90] Korean -Spanish TB [91] Spanish 68,000 IT-TimeBank [93] Italian 150,000 TimeBank-PT [89] Portuguese 70,000 Ro-TimeBank [92] Romanian 65,375 Arabic TB [73] Arabic 95,782…”

Section: Temporal Extraction For Non-english Languagesmentioning

confidence: 99%

Event-Centric Temporal Knowledge Graph Construction: A Survey

Knez,

Žitnik

2023

Mathematics

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Textual documents serve as representations of discussions on a variety of subjects. These discussions can vary in length and may encompass a range of events or factual information. Present trends in constructing knowledge bases primarily emphasize fact-based common sense reasoning, often overlooking the temporal dimension of events. Given the widespread presence of time-related information, addressing this temporal aspect could potentially enhance the quality of common-sense reasoning within existing knowledge graphs. In this comprehensive survey, we aim to identify and evaluate the key tasks involved in constructing temporal knowledge graphs centered around events. These tasks can be categorized into three main components: (a) event extraction, (b) the extraction of temporal relationships and attributes, and (c) the creation of event-based knowledge graphs and timelines. Our systematic review focuses on the examination of available datasets and language technologies for addressing these tasks. An in-depth comparison of various approaches reveals that the most promising results are achieved by employing state-of-the-art models leveraging large pre-trained language models. Despite the existence of multiple datasets, a noticeable gap exists in the availability of annotated data that could facilitate the development of comprehensive end-to-end models. Drawing insights from our findings, we engage in a discussion and propose four future directions for research in this domain. These directions encompass (a) the integration of pre-existing knowledge, (b) the development of end-to-end systems for constructing event-centric knowledge graphs, (c) the enhancement of knowledge graphs with event-centric information, and (d) the prediction of absolute temporal attributes.

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“…We based our temporal relation extraction pipeline on his, as described in Section 4. Jeong et al (2015) and Mirza and Minard (2014) developed systems for addressing temporal information extraction in Korean and Italian, respectively. Ling and Weld (2010) and Glavas and Snajder (2015) addressed a temporal information extraction problem (in English) similar to, but not the same as, that defined in TempEval-3.…”

Section: Variations Of Temporal Information Extractionmentioning

confidence: 99%

Annotation projection for temporal information extraction

2019

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Approaches to building temporal information extraction systems typically rely on large, manually annotated corpora. Thus, porting these systems to new languages requires acquiring large corpora of manually annotated documents in the new languages. Acquiring such corpora is difficult owing to the complexity of temporal information extraction annotation. One strategy for addressing this difficulty is to reduce or eliminate the need for manually annotated corpora through annotation projection. This technique utilizes a temporal information extraction system for a source language (typically English) to automatically annotate the source language side of a parallel corpus. It then uses automatically generated word alignments to project the annotations, thereby creating noisily annotated target language training data. We developed an annotation projection technique for producing target language temporal information extraction systems. We carried out an English (source) to French (target) case study wherein we compared a French temporal information extraction system built using annotation projection with one built using a manually annotated French corpus. While annotation projection has been applied to building other kinds of Natural Language Processing tools (e.g., Named Entity Recognizers), to our knowledge, this is the first paper examining annotation projection as applied to temporal information extraction where no manual corrections of the target language annotations were made. We found that, even using manually annotated data to build a temporal information extraction system, F-scores were relatively low (<0.35), which suggests that the problem is challenging even with manually annotated data. Our annotation projection approach performed well (relative to the system built from manually annotated data) on some aspects of temporal information extraction (e.g., event–document creation time temporal relation prediction), but it performed poorly on the other kinds of temporal relation prediction (e.g., event–event and event–time).

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Temporal Information Extraction from Korean Texts

Cited by 9 publications

References 10 publications

Prediction of Postoperative Complications for Patients of End Stage Renal Disease

Prediction of Postoperative Complications for Patients of End Stage Renal Disease

Event-Centric Temporal Knowledge Graph Construction: A Survey

Annotation projection for temporal information extraction

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