Investigating Machine Learning Approaches for Sentence Compression in Different Application Contexts for Portuguese

Nóbrega, Fernando Antônio Asevedo; Pardo, Thiago Alexandre Salgueiro

doi:10.1007/978-3-319-41552-9_25

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…There are mainly two different methods to solve a compression task of a sentence. While abstractive approaches [13][14] rely on paraphrasing words, extractive methods [6], [8][9], [15][16][17][18][19][20] solve sentence compression as a sequence of word deletions of the original sentence. In this case, for each word of a sentence, the compression algorithm needs to decide whether to keep or delete the word based on its given features [17].…”

Section: Related Workmentioning

confidence: 99%

“…Lai, et al [16] propose a bi-directional encoder-decoder approach while Thao, et al [18] use a gated neural network. ML models dedicated to classifying the word tokens of a sentence that will be omitted are also investigated in [19]. The approach aims at applying sentence compression in a cross-lingual setting learning from sentences in two different languages, English and Portuguese.…”

Section: Related Workmentioning

confidence: 99%

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Using Artificial Neural Networks to Derive Process Model Activity Labels from Process Descriptions

Pyrtek¹,

Hake²,

Loos³

2020

WI2020 Zentrale Tracks

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Recently, Artificial Neural Networks (ANN) have shown high potential in the area of Natural Language Processing (NLP). In the area of sentence compression, the application of ANNs has proven to outperform existing rule-based approaches. Nevertheless, these approaches require a decent amount of training data to achieve high accuracy. In this work, we aim at employing ANNs to derive process model labels from process descriptions. Since the amount of publicly available pairs of text and process model is scarce, we employ a transfer learning approach. While training the compression model on a large corpus consisting of sentence-compression pairs, we transfer the model to the problem of deriving label descriptions. We implement our approach and conduct an experimental evaluation using pairs of process descriptions and models. We found that our transfer learning model keeps high recall while losing performance on precision and compression rate.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Using Artificial Neural Networks to Derive Process Model Activity Labels from Process Descriptions

Pyrtek¹,

Hake²,

Loos³

2020

WI2020 Zentrale Tracks

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“…The concept of a sentence in written or spoken texts is important in several Natural Language Processing (NLP) tasks, such as morpho-syntactic analysis [Kepler andFinger 2010, Fonseca andAluísio 2016], sentiment analysis [Anchiêta et al 2015, Brum et al 2016, summarization [Nóbrega and Pardo 2016], and speech processing [Mendonc ¸a et al 2014], among others. However, punctuation marks that constitute a sentence boundary are ambiguous The Disambiguation of Punctuation Marks (DPM) task analyzes punctuation marks in texts and indicates whether they correspond to a sentence boundary.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts

Treviso,

Shulby,

Aluisio

2017

Preprint

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This paper is motivated by the automation of neuropsychological tests involving discourse analysis in the retellings of narratives by patients with potential cognitive impairment. In this scenario the task of sentence boundary detection in speech transcripts is important as discourse analysis involves the application of Natural Language Processing tools, such as taggers and parsers, which depend on the sentence as a processing unit. Our aim in this paper is to verify which embedding induction method works best for the sentence boundary detection task, specifically whether it be those which were proposed to capture semantic, syntactic or morphological similarities.

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“…O conceito de sentença em textos escritos ou falados é importante em diversas tarefas de PLN, como análise morfossintática FINGER, 2010;, atribuição de autoria (MARINHO; HIRST; AMANCIO, 2016), análise de sentimentos (ANCHI-ÊTA et al, 2015;ARAUJO;KEPLER, 2016), sumarização (NÓBREGA; PARDO, 2016), entre outros. Entretanto, os sinais de pontuação que constituem um limite de sentença são ambíguos; por exemplo, na maioria dos idiomas o ponto final (".")…”

Section: Desambiguação De Sinais De Pontuaçãounclassified

Segmentação de sentenças e detecção de disfluências em narrativas transcritas de testes neuropsicológicos

Treviso¹

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Investigating Machine Learning Approaches for Sentence Compression in Different Application Contexts for Portuguese

Cited by 7 publications

References 5 publications

Using Artificial Neural Networks to Derive Process Model Activity Labels from Process Descriptions

Using Artificial Neural Networks to Derive Process Model Activity Labels from Process Descriptions

Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts

Segmentação de sentenças e detecção de disfluências em narrativas transcritas de testes neuropsicológicos

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