Current approaches to punctuation in computational linguistics

Say, Bilge; Akman, Varol

doi:10.1007/bf00057941

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…The reader is referred to (Say 1995) for a detailed review of punctuation. Other recent papers of our group which may also be useful include (Bayraktar et al 1996;Say and Akman 1996b;Say and Akman 1996a) .…”

Section: Resultsmentioning

confidence: 99%

An Information-Based Treatment of Punctuation in Discourse Representation Theory

Say¹,

Akman²

1998

Studies in Functional and Structural Linguistics

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Punctuation has so far attracted attention within the linguistics com munity mostly from a syntactic perspective. In this paper, we give a preliminary account of the information-based aspects of punctuation, drawing our points from assorted, naturally-occurring sentences. We present our formal models of these sentences and the semantic contri butions of punctuation marks. Our formalism is a simplified analogue of an extension-due to Nicholas Asher-of Discourse Representa tion Theory.

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Section: Resultsmentioning

confidence: 99%

An Information-Based Treatment of Punctuation in Discourse Representation Theory

Say¹,

Akman²

1998

Studies in Functional and Structural Linguistics

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“…Structural punctuation for the purpose of this analysis are marks that act on parts of documents typically not larger than a sentence and not smaller than a word (e.g. hyphens) (Say andAkman 1996, Meyer 1987). The central idea of our model is to use the observed structural information in text presented by punctuation and conjunctions for inference regarding the dynamics of topics in text.…”

Section: Previous Researchmentioning

confidence: 99%

Improving Text Analysis Using Sentence Conjunctions and Punctuation

Büschken

Allenby

2020

Marketing Science

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User-generated content in the form of customer reviews, blogs, and tweets is an emerging and rich source of data for marketers. Topic models have been successfully applied to such data, demonstrating that empirical text analysis benefits greatly from a latent variable approach that summarizes high-level interactions among words. We propose a new topic model that allows for serial dependency of topics in text. That is, topics may carry over from word to word in a document, violating the bag-of-words assumption in traditional topic models. In the proposed model, topic carryover is informed by sentence conjunctions and punctuation. Typically, such observed information is eliminated prior to analyzing text data (i.e., preprocessing) because words such as “and” and “but” do not differentiate topics. We find that these elements of grammar contain information relevant to topic changes. We examine the performance of our models using multiple data sets and establish boundary conditions for when our model leads to improved inference about customer evaluations. Implications and opportunities for future research are discussed.

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“…However, without punctuation it is quite difficult to understand the context of the narrative. Researchers have identified various methods for adding punctuation in a text corpus (Say and Akman 1996), and recent developments have seen neural network based approaches. Authors in (Tilk and Alumäe 2016) discussed a recurrent bidirectional neural network for missing punctuation.…”

Section: Semantic Inferencementioning

confidence: 99%

GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response

Rahman

Preum

Williams

et al. 2020

AAAI

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EMS (emergency medical service) plays an important role in saving lives in emergency and accident situations. When first responders, including EMS providers and firefighters, arrive at an incident, they communicate with the patients (if conscious), family members and other witnesses, other first responders, and the command center. The first responders utilize a microphone and headset to support these communications. After the incident, the first responders are required to document the incident by filling out a form. Today, this is performed manually. Manual documentation of patient summary report is time-consuming, tedious, and error-prone. We have addressed these form filling problems by transcribing the audio from the scene, identifying the relevant information from all the conversations, and automatically filling out the form. Informal survey of first responders indicate that this application would be exceedingly helpful to them. Results show that we can fill out a model summary report form with an F1 score as high as 94%, 78%, 96%, and 83% when the data is noise-free audio, noisy audio, noise-free textual narratives, and noisy textual narratives, respectively.

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Current approaches to punctuation in computational linguistics

Cited by 11 publications

References 23 publications

An Information-Based Treatment of Punctuation in Discourse Representation Theory

An Information-Based Treatment of Punctuation in Discourse Representation Theory

Improving Text Analysis Using Sentence Conjunctions and Punctuation

GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response

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