Predicting sentences using N-gram language models

Bickel, Steffen; Haider, Peter; Scheffer, Tobias

doi:10.3115/1220575.1220600

Cited by 38 publications

(27 citation statements)

References 12 publications

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“…Ngram based language models such as unigram, bigram, trigram, linear interpolation and backoff models are proposed for auto completing a sentence by predicting a single word in a sentence which is different than the work presented in this paper because now the prediction is built with a set of words instead of a single one during finding out the best language model. In [5], the authors developed a sentence completion method in both German and English based on N-gram language models and they derived a k best Viterbi beam search decoder for strongly completing a sentence. The use of Artificial Intelligence for word prediction in Spanish is also observed in [6], in which using the chart bottom-up technique, syntactic and semantic analysis is done for word prediction.…”

Section: Related Workmentioning

confidence: 99%

An Exploratory Approach to Find a Novel Metric Based Optimum Language Model for Automatic Bangla Word Prediction

Habib¹,

Al‐Mamun²,

Rahman³

et al. 2018

IJISA

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Abstract-Word completion and word prediction are two important phenomena in typing that have intense effect on aiding disable people and students while using keyboard or other similar devices. Such auto completion technique also helps students significantly during learning process through constructing proper keywords during web searching. A lot of works are conducted for English language, but for Bangla, it is still very inadequate as well as the metrics used for performance computation is not rigorous yet. Bangla is one of the mostly spoken languages (3.05% of world population) and ranked as seventh among all the languages in the world. In this paper, word prediction on Bangla sentence by using stochastic, i.e. N-gram based language models are proposed for auto completing a sentence by predicting a set of words rather than a single word, which was done in previous work. A novel approach is proposed in order to find the optimum language model based on performance metric. In addition, for finding out better performance, a large Bangla corpus of different word types is used.

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Section: Related Workmentioning

confidence: 99%

An Exploratory Approach to Find a Novel Metric Based Optimum Language Model for Automatic Bangla Word Prediction

Habib¹,

Al‐Mamun²,

Rahman³

et al. 2018

IJISA

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“…In an analysis of predicting sentences [2] researcher developed a sentence completion method based on N-gram language models and they derived a k best Viterbi beam search decoder for strongly completing a sentence. We also observed use of Artificial Intelligence [3] for word prediction.…”

Section: Related Workmentioning

confidence: 99%

Automated Word Prediction in Bangla Language Using Stochastic Language Models

Haque¹,

Habib²,

Rahman³

2015

IJFCST

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“…It is crucial to most of NLP applications, including final word prediction [4], language identification [5], information retrieval [6], speech recognition [7], machine translation [8], part-of-speech (POS) tagging [9], or sentiment analysis [10].…”

Section: Introductionmentioning

confidence: 99%

Experiments with language combinatorics in text classification: lessons learned and future implications

2017

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This paper presents a meta-analysis of experiments performed with language combinatorics (LC), a novel language model generation and feature extraction method based on combinatorial manipulations of sentence elements (e.g., words). Along recent years LC has been applied to a number of text classification tasks, such as affect analysis, cyberbullying detection or future reference extraction. We summarize two of the most extensive experiments and discuss general implications for future implementations of combinatorial language model. Keywords: language combinatorics, natural language processing, text classification Streszczenie W niniejszym artykule przedstawiono metaanalizę badań przeprowadzonych za pomocą kombinatoryki językowej (language combinatorics, LC), nowej metody generacji modelu języka i ekstrakcji cech, opartej o kombinacyjne manipulacje na elementach zdań (np. słowa). W trakcie ostatnich lat LC została zastosowana do wielu zadań z dziedziny klasyfikacji tekstu, takich jak analiza afektu, wykrywanie cyberagresji lub ekstrakcja odniesień do przyszłych wydarzeń. W niniejszym artykule podsumowujemy dwa z najbardziej obszernych doświadczeń i omawiamy ogólne implikacje dotyczące przyszłych zastosowań kombinatoryjnego modelu języka.Słowa kluczowe: kombinatoryka językowa, przetwarzanie języków naturalnych, klasyfikacja tekstu 184

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Predicting sentences using N-gram language models

Cited by 38 publications

References 12 publications

An Exploratory Approach to Find a Novel Metric Based Optimum Language Model for Automatic Bangla Word Prediction

An Exploratory Approach to Find a Novel Metric Based Optimum Language Model for Automatic Bangla Word Prediction

Automated Word Prediction in Bangla Language Using Stochastic Language Models

Experiments with language combinatorics in text classification: lessons learned and future implications

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