Using Inference for Evaluating Models of Temporal Discourse

Muller, Philippe

doi:10.1109/time.2005.39

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Currently, we consider the process fragments are in sequence but, in the future, we want to order the sentences and the independent clauses based on time structures. There is already research in this direction, one example being Stanford Temporal Tagger [33] or the work of Muller [34].…”

Section: ) Activityrelationshipminermentioning

confidence: 99%

Automatic process model discovery from textual methodologies

Epure

Martín-Rodilla

Hug

et al. 2015

2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS)

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Process mining has been successfully used in automatic knowledge discovery and in providing guidance or support. The known process mining approaches rely on processes being executed with the help of information systems thus enabling the automatic capture of process traces as event logs. However, there are many other fields such as Humanities, Social Sciences and Medicine where workers follow processes and log their execution manually in textual forms instead. The problem we tackle in this paper is mining process instance models from unstructured, text-based process traces. Using natural language processing with a focus on the verb semantics, we created a novel unsupervised technique TextProcessMiner that discovers process instance models in two steps: 1.ActivityMiner mines the process activities; 2.ActivityRelationshipMiner mines the sequence, parallelism and mutual exclusion relationships between activities. We employed technical action research through which we validated and preliminarily evaluated our proposed technique in an Archaeology case. The results are very satisfactory with 88% correctly discovered activities in the log and a process instance model that adequately reflected the original process. Moreover, the technique we created emerged as domain independent.

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Section: ) Activityrelationshipminermentioning

confidence: 99%

Automatic process model discovery from textual methodologies

Epure

Martín-Rodilla

Hug

et al. 2015

2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS)

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“…Section 1 covers the basic concepts followed by predefinitions in Section 2, and Section 3 covers the concept of time tagging. The 2 major temporal logics, Reichenbach's temporal logic (RTL) [25][26][27][28][29] and Allen's temporal logic (ATL), are explained before TimeML. Moreover, in the literature, both ATL and Allen's interval logic (AIL) are well accepted, and in this paper, ATL will be used.…”

Section: Introductionmentioning

confidence: 99%

Temporal logic extension for self-referring, nonexistence, multiple recurrence, and anterior past events

Şeker¹

2015

Turk J Elec Eng & Comp Sci

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This study focuses on the possible extensions of current temporal logics. In this study, 4 extensions are proposed: self-referring events, nonexisting events, multiple recurrence of events, and an improvement on anterior past events. Each of these extensions is on a different level of temporal logics. The main motivation behind the extensions is the temporal analysis of Turkish. Similar to temporal logic studies built on other natural languages, like French, Ukrainian, Italian, Korean, English, or Romanian, this is the first time that the Turkish language has been deeply questioned in the sense of computable temporal logic using the view of a standardized temporal markup language.This study keeps the methodology of TimeML and researches Turkish from the perspectives of Reichenbach and Allen's temporal logics. Reichenbach's temporal logic is perfectly capable of handling the anterior temporal feeling, but it is not enough to handle the sense of 'learnt' or 'study', which are 2 past tenses in Turkish. Moreover, Allen's temporal logic cannot handle 2 events following each other continuously, which is called recurring events in this study for the first time.Finally, based on the experiences from a 4-year PhD study on natural language texts, this study underlines the absence of self-referring or a reference to nonexisting events in temporal logics. After adding the above extensions to computable temporal logic, the capability of tagging the events in Turkish texts is measured with an increase from 18% to 100%, creating a Turkish corpus for the first time. Moreover, new software is implemented to visualize the tagged events and previous software is developed to handle events tagged for Turkish.

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“…In similar work on TimeML annotations,Setzer et al (2003;Muller and Raymonet (2005) add implied relations to annotated, temporal graphs.…”

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confidence: 99%

Testing SDRT's Right Frontier

Afantenos,

Asher

2010

Preprint

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The Right Frontier Constraint (RFC), as a constraint on the attachment of new constituents to an existing discourse structure, has important implications for the interpretation of anaphoric elements in discourse and for Machine Learning (ML) approaches to learning discourse structures. In this paper we provide strong empirical support for SDRT's version of RFC. The analysis of about 100 doubly annotated documents by five different naive annotators shows that SDRT's RFC is respected about 95% of the time. The qualitative analysis of presumed violations that we have performed shows that they are either click-errors or structural misconceptions.

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Using Inference for Evaluating Models of Temporal Discourse

Abstract: Abstract

Cited by 6 publications

References 14 publications

Automatic process model discovery from textual methodologies

Automatic process model discovery from textual methodologies

Temporal logic extension for self-referring, nonexistence, multiple recurrence, and anterior past events

Testing SDRT's Right Frontier

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