Papers by Mustafa Ocal
A Comprehensive Evaluation and Correction of the TimeBank Corpus (2022.lrec-1)
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| Challenge: | TimeML is an annotation scheme for capturing temporal information in text. |
| Approach: | They propose to use TimeML to validate TimeML and provide a rich dataset of events, temporal expressions, and temporal relationships for training and testing temporal analysis systems. |
| Outcome: | The proposed methods detect and correct errors in the TimeML corpus and provide a reference corpus for training and testing temporal analysis systems. |
jTLEX: a Java Library for TimeLine EXtraction (2023.eacl-demo)
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| Challenge: | Timeline EXtraction library provides Java implementation of TimeML annotations and tools for programmatic manipulation of Timeline graphs. |
| Approach: | jTLEX provides a Java implementation of TimeLine EXtraction algorithm and utilities for programmatic manipulation of TimeML graphs. |
| Outcome: | jTLEX provides a Java implementation of the TimeLine EXtraction algorithm, along with utilities for programmatic manipulation of TimeML graphs. |
Holistic Evaluation of Automatic TimeML Annotators (2022.lrec-1)
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| Challenge: | TimeML is an annotation scheme for representing temporal information in texts. |
| Approach: | They propose to combine eight metrics for holistic evaluation of TimeML graphs. |
| Outcome: | The proposed system produces graphs with 1/3 of the time indeterminacy and 1/3 of gold standard . the proposed system is compared with four other systems and is a good fit for the proposed task. |
pyTLEX: A Python Library for TimeLine EXtraction (2024.eacl-demo)
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| Challenge: | TimeML is a markup language for temporal information in text. |
| Approach: | pyTLEX is an implementation of the TimeLine EXtraction algorithm . it allows users to parse TimeML annotations, construct TimeML graphs, and execute the algorithm based on TimeML . |
| Outcome: | pyTLEX is an implementation of the TimeLine EXtraction algorithm . it allows users to parse TimeML annotations, construct TimeML graphs, and execute the algorithm to effect complete timeline extraction. |
Evaluating Information Loss in Temporal Dependency Trees (2020.lrec-1)
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| Challenge: | Temporal Dependency Trees (TDTs) are an alternative to full temporal graphs for representing the temporal structure of texts. |
| Approach: | They propose a method to quantify temporal indeterminacy using temporal constraint problems to extract timelines from temporal graphs. |
| Outcome: | The proposed method shows that the tree form of TDTs results in a 109% increase in temporal indeterminacy over their corresponding temporal graphs. |