Temporal Reasoning in Natural Language Processing: A Survey

Sanampudi, Suresh Kumar; Kumari, G. Vijaya

doi:10.5120/100-209

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…The state-of-art of Temporal Reasoning proposes several constraint-based models [16,17]. The events in the text forms the nodes of the network and the links forms the temporal relations in which the order of these events occur.…”

Section: -Literature Reviewmentioning

confidence: 99%

A Novel Framework for Multi-Document Temporal Summarization (MDTS)

Mamidala

Sanampudi

2021

Emerg Sci J

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Internet or Web consists of a massive amount of information, handling which is a tedious task. Summarization plays a crucial role in extracting or abstracting key content from multiple sources with its meaning contained, thereby reducing the complexity in handling the information. Multi-document summarization gives the gist of the content collected from multiple documents. Temporal summarization concentrates on temporally related events. This paper proposes a Multi-Document Temporal Summarization (MDTS) technique that generates the summary based on temporally related events extracted from multiple documents. This technique extracts the events with the time stamp. TIMEML standards tags are used in extracting events and times. These event-times are stored in a structured database form for easier operations. Sentence ranking methods are build based on the frequency of events occurrences in the sentence. Sentence similarity measures are computed to eliminate the redundant sentences in an extracted summary. Depending on the required summary length, top-ranked sentences are selected to form the summary. Experiments are conducted on DUC 2006 and DUC 2007 data set that was released for multi-document summarization task. The extracted summaries are evaluated using ROUGE to determine precision, recall and F measure of generated summaries. The performance of the proposed method is compared with particle swarm optimization-based algorithm (PSOS), Cat swarm optimization-based summarization (CSOS), Cuckoo Search based multi-document summarization (MDSCSA). It is found that the performance of MDTS is better when compared with other methods. Doi: 10.28991/esj-2021-01268 Full Text: PDF

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Section: -Literature Reviewmentioning

confidence: 99%

A Novel Framework for Multi-Document Temporal Summarization (MDTS)

Mamidala

Sanampudi

2021

Emerg Sci J

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“…There is a body of research on temporal reasoning [16]. For example, Russell et al describe an event calculus [15].…”

Section: Related Workmentioning

confidence: 99%

Deriving time lines from texts

Landhäußer¹,

Hey²,

Tichy³

2014

Proceedings of the 3rd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering

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We investigate natural language as an alternative to programming languages. Natural language would empower anyone to program with minimal training. In this paper, we solve an ordering problem that arises in natural-language programming. An emprical study showed that users do not always provide the strict sequential order of steps needed for execution on a computer. Instead, temporal expressions involving "before", "after", "while", "at the end", and others are used to indicate an order other than the textual one. We present an analysis that extracts the intended time line by exploiting temporal clues. The technique is analyzed in the context of Alice, a 3D programming environment, and AliceNLP, a system for programming Alice in ordinary English. Extracting temporal order could also be useful for analyzing reports, question answering, help desk requests, and big data applications.

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“…Different TCNs are defined depending on the representation of the temporal entity as time intervals, durations or points, and the class of constraints namely qualitative, quantitative, metrics or its combination [31]. Several researches fall into this framework [26] [32] [33].…”

Section: Temporal Reasoningmentioning

confidence: 99%

Temporal Information Processing: A Survey

Berrazega¹

2012

IJNLC

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Temporal Information Processing is a subfield of Natural Language Processing, valuable in many tasks like Question Answering and Summarization. Temporal Information Processing is broadened, ranging from classical theories of time and language to current computational approaches for Temporal Information Extraction. This later trend consists on the automatic extraction of events and temporal expressions. Such issues have attracted great attention especially with the development of annotated corpora and annotations schemes mainly TimeBank and TimeML. In this paper, we give a survey of Temporal Information Extraction from Natural Language texts.

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Temporal Reasoning in Natural Language Processing: A Survey

Cited by 13 publications

References 6 publications

A Novel Framework for Multi-Document Temporal Summarization (MDTS)

A Novel Framework for Multi-Document Temporal Summarization (MDTS)

Deriving time lines from texts

Temporal Information Processing: A Survey

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