| 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. |
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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. |
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. |
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NarrativeTime: Dense Temporal Annotation on a Timeline (2024.lrec-main)
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| Challenge: | e.g. TimeBank contains 1-5% of all possible tlinks, and this information is underspecified in the text. |
| Approach: | They propose a timeline-based framework that achieves full coverage of all possible TLINKs. |
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PyVallex: A Processing System for Valency Lexicon Data (2020.lrec-1)
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| Challenge: | PyVallex is a Python-based system for presenting, searching, filtering, editing and processing machine-readable lexicon data . the system provides most of the typical functionalities of a Dictionary Writing System (DWS) |
| Approach: | PyVallex is a Python-based system for presenting, searching, editing and processing machine-readable lexicon data. |
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text2story: A Python Toolkit to Extract and Visualize Story Components of Narrative Text (2024.lrec-main)
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| Challenge: | Story components, namely events, time, participants, and their relations, are present in narrative texts from different domains such as journalism, medicine, finance, and law. |
| Approach: | They propose to use an array of narrative extraction tools to extract narratives from text . the package contains an array and an experimental module for evaluation . |
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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. |
EventPlus: A Temporal Event Understanding Pipeline (2021.naacl-demos)
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Mingyu Derek Ma, Jiao Sun, Mu Yang, Kung-Hsiang Huang, Nuan Wen, Shikhar Singh, Rujun Han, Nanyun Peng
| Challenge: | Event information is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. |
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Temporal Information Extraction by Predicting Relative Time-lines (D18-1)
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| Challenge: | a new paradigm for temporal information extraction from text evades the relation extraction phase because there are n 2 possible entity pairs in a text with n temporal entities. |
| Approach: | They propose a method to construct a linear time-line from a set of temporal relations from text without the intermediate step of prediction of tempor relations. |
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Comprehensive Annotation of Various Types of Temporal Information on the Time Axis (L18-1)
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| Challenge: | Existing studies linking event and time information have been conducted to train and evaluate models. |
| Approach: | They propose an annotation scheme that anchors expressions in text to the time axis comprehensively. |
| Outcome: | The proposed scheme can be utilized for integrated information analysis of events, entities and time. |
ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks (2024.acl-long)
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| Challenge: | Existing models overlook the temporal dimension in their training process, leading to suboptimal performance over time. |
| Approach: | They propose a training paradigm that trains models on chronological splits, preserving the temporal order of the data. |
| Outcome: | The proposed model fails to fit to recent data, despite continual learning and temporal invariant methods. |