Word2vec semantic representation in multilabel classification for Indonesian news article

Rahmawati, Dyah; Khodra, Masayu Leylia

doi:10.1109/icaicta.2016.7803115

Cited by 23 publications

(24 citation statements)

References 8 publications

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“…The initial evaluations were passed only by using the baseline structure of GloVe embedding along with LSTM layers which proved to be costly as the micro F1 score that I got was comparatively less (about 0.57 for phase I and 0.61 for phase II) whereas the training time for significantly high. So, the accuracy of the model was improved through maintaining the semantic and syntactic features of statements intact by using the two novel types of research in the Natural Language Processing field which are Word2Vec(Word to Vector) (Mikolov et al, 2013;Rahmawati and Khodra, 2016) and GloVe(Global Vectors)( Baroni et al, 2014;Pennington et al, 2014) embedding layers. The Word2Vec embedding layers maintained the sentiments of the provided text whereas the GloVe embedding maintained the semantic feature of the text.…”

Section: Embedding Layersmentioning

confidence: 99%

E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text

Patel¹

2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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This paper discusses the solution to the problem statement of the SemEval19: EmoContext competition(Chatterjee et al., 2019b) which is "Contextual Emotion Detection in Texts". The paper includes the explanation of an architecture that I created by exploiting the embedding layers of Word2Vec and GloVe using LSTMs as memory unit cells which detects approximate emotion of chats between two people in the English language provided in the textual form. The set of emotions on which the model was trained was Happy, Sad, Angry and Others. The paper also includes an analysis of different conventional machine learning algorithms in comparison to E-LSTM.

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Section: Embedding Layersmentioning

confidence: 99%

E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text

Patel¹

2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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“…To address this problem, the word representation feature was employed, which handles the semantics of words. Several works have shown that a semantic model can produce higher performance than a lexicon [12,13] because it captures the weight of similarities between words in a document. Therefore, this research conducted rhetorical sentence categorization by employing word embedding to detect the semantic meaning of words in scientific articles [7,14].…”

Section: Introductionmentioning

confidence: 99%

Word Embedding for Rhetorical Sentence Categorization on Scientific Articles

2018

Self Cite

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A common task in summarizing scientific articles is employing the rhetorical structure of sentences. Determining rhetorical sentences itself passes through the process of text categorization. In order to get good performance, some works in text categorization have been done by employing word embedding. This paper presents rhetorical sentence categorization of scientific articles by using word embedding to capture semantically similar words. A comparison of employing Word2Vec and GloVe is shown. First, two experiments are evaluated using five classifiers, namely Naïve Bayes, Linear SVM, IBK, J48, and Maximum Entropy. Then, the best classifier from the first two experiments was employed. This research showed that Word2Vec CBOW performed better than Skip-Gram and GloVe. The best experimental result was from Word2Vec CBOW for 20,155 resource papers from ACL-ARC, features from Teufel and the previous label feature. In this experiment, Linear SVM produced the highest F-measure performance at 43.44%.

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“…Munculnya banyak situs berita yang ada di Indonesia menyebabkan pertumbuhan berita yang cepat, banyak, dan tidak terkelompok. Situs news aggregator saat ini telah banyak berkembang dan telah secara otomatis melakukan pengelompokan berita berdasarkan topik yang sesuai [2]. Kategorisasi berita tersebut dibantu oleh komputer dengan algoritme klasifikasi dokumen.…”

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“…Hasil penggunaan Word2Vec sebagai fitur di penelitian yang lalu memiliki kinerja lebih baik jika dibandingkan dengan teknik representasi yang lain [20]. Penggunaan Word2Vec untuk klasifikasi dokumen berita di bahasa Indonesia juga telah diusulkan [2]. Hasil percobaan klasifikasi di bahasa Indonesia memberikan angka F1-Score terbaik, yaitu 81,10% dengan model yang digunakan adalah Skip-Gram dengan dimensi 200.…”

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Self-Training Naive Bayes Berbasis Word2Vec untuk Kategorisasi Berita Bahasa Indonesia

Santoso¹,

Soetiono²,

Gunawan³

et al. 2018

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Abstract-News as one kind of information that is needed in daily life has been available on the internet. News website often categorizes their articles to each topic to help users access the news more easily. Document classification has widely used to do this automatically. The current availability of labeled training data is insufficient for the machine to create a good model. The problem in data annotation is that it requires a considerable cost and time to get sufficient quantity of labeled training data. A semi-supervised algorithm is proposed to solve this problem by using labeled and unlabeled data to create classification model. This paper proposes semi-supervised learning news classification system using Self-Training Naive Bayes algorithm. The feature that is used in text classification is Word2Vec Skip-Gram Model. This model is widely used in computational linguistics or text mining research as one of the methods in word representation. Word2Vec is used as a feature because it can bring the semantic meaning of the word in this classification task. The data used in this paper consists of 29,587 news documents from Indonesian online news websites. The Self-Training Naive Bayes algorithm achieved the highest F1-Score of 94.17%.Intisari-Berita sebagai salah satu jenis informasi yang dibutuhkan dalam kehidupan sehari-hari telah tersedia secara bebas di internet. Situs berita telah melakukan pengelompokan berita berdasarkan topiknya untuk mempermudah pengguna mencari berita yang dibutuhkan. Klasifikasi dokumen telah banyak digunakan untuk membantu pengelompokan berita secara otomatis. Kurang tersedianya data pelatihan yang cukup untuk digunakan komputer membentuk model klasifikasi yang baik sering menjadi kendala dalam implementasi di kasus nyata. Masalah utama dalam pelabelan data pelatihan agar diperoleh jumlah data yang cukup adalah perlunya biaya yang besar dan waktu yang cukup lama. Algoritme semi-supervised telah ditawarkan untuk menjawab permasalahan tersebut dengan menggunakan data berlabel dan tak berlabel dalam membentuk model klasifikasi yang dibutuhkan. Makalah ini mengusulkan sistem klasifikasi berita menggunakan semi-supervised learning dengan algoritme Self-Training Naive Bayes.

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Word2vec semantic representation in multilabel classification for Indonesian news article

Cited by 23 publications

References 8 publications

E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text

E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text

Word Embedding for Rhetorical Sentence Categorization on Scientific Articles

Self-Training Naive Bayes Berbasis Word2Vec untuk Kategorisasi Berita Bahasa Indonesia

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