Language Model Based on Word Order Sensitive Matrix Representation in Latent Semantic Analysis for Speech Recognition

Abstract The aim of works described in this article is to elaborate and experimentally evaluate a consistent method of Language Model (LM) construction for the sake of Polish speech recognition. In the proposed method we tried to take into account the features and specific problems experienced in practical applications of speech recognition in the Polish language, reach inflection, a loose word order and the tendency for short word deletion. The LM is created in five stages. Each successive stage takes the model prepared at the previous stage and modifies or extends it so as to improve its properties. At the first stage, typical methods of LM smoothing are used to create the initial model. Four most frequently used methods of LM construction are here. At the second stage the model is extended in order to take into account words indirectly co-occurring in the corpus. At the next stage, LM modifications are aimed at reduction of short word deletion errors, which occur frequently in Polish speech recognition. The fourth stage extends the model by insertion of words that were not observed in the corpus. Finally the model is modified so as to assure highly accurate recognition of very important utterances. The performance of the methods applied is tested in four language domains.

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Pembentukan Vector Space Model Bahasa Indonesia Menggunakan Metode Word to Vector

Jurnal Buana Informatika ◽

10.24002/jbi.v10i1.2053 ◽

2019 ◽

Vol 10 (1) ◽

pp. 29

Author(s):

Yulius Denny Prabowo ◽

Tedi Lesmana Marselino ◽

Meylisa Suryawiguna

Keyword(s):

Vector Space ◽

Latent Semantic Analysis ◽

Semantic Analysis ◽

Language Model ◽

Vector Space Model ◽

Online News ◽

Bag Of Words ◽

Space Model ◽

Language Research ◽

Bahasa Indonesia

Extracting information from a large amount of structured data requires expensive computing. The Vector Space Model method works by mapping words in continuous vector space where semantically similar words are mapped in adjacent vector spaces. The Vector Space Model model assumes words that appear in the same context, having the same semantic meaning. In the implementation, there are two different approaches: counting methods (eg: Latent Semantic Analysis) and predictive methods (eg Neural Probabilistic Language Model). This study aims to apply Word2Vec method using the Continuous Bag of Words approach in Indonesian language. Research data was obtained by crawling on several online news portals. The expected result of the research is the Indonesian words vector mapping based on the data used.Keywords: vector space model, word to vector, Indonesian vector space model.Ekstraksi informasi dari sekumpulan data terstruktur dalam jumlah yang besar membutuhkan komputasi yang mahal. Metode Vector Space Model bekerja dengan cara memetakan kata-kata dalam ruang vektor kontinu dimana kata-kata yang serupa secara semantis dipetakan dalam ruang vektor yang berdekatan. Metode Vector Space Model mengasumsikan kata-kata yang muncul pada konteks yang sama, memiliki makna semantik yang sama. Dalam penerapannya ada dua pendekatan yang berbeda yaitu: metode yang berbasis hitungan (misal: Latent Semantic Analysis) dan metode prediktif (misalnya Neural Probabilistic Language Model). Penelitian ini bertujuan untuk menerapkan metode Word2Vec menggunakan pendekatan Continuous Bag Of Words model dalam Bahasa Indonesia. Data penelitian yang digunakan didapatkan dengan cara crawling pada berberapa portal berita online. Hasil penelitian yang diharapkan adalah pemetaan vektor kata Bahasa Indonesia berdasarkan data yang digunakan.Kata Kunci: vector space model, word to vector, vektor kata bahasa Indonesia.

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