Sentence Clustering Using Continuous Vector Space Representation

Chinea-Rios, Mara; Sanchis-Trilles, Germán; Casacuberta, Francisco

doi:10.1007/978-3-319-19390-8_49

Cited by 2 publications

(2 citation statements)

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“…The mixture components of the words capture the notion of latent topics here. [15] uses K-means to cluster the sentences based on vector representations derived as a weighted sum of the word embeddings. It uses intra-cluster perplexity and F1 scores to measure the effectiveness of the clustering.…”

Section: Related Studymentioning

confidence: 99%

Intent Discovery Through Unsupervised Semantic Text Clustering

Padmasundari¹,

Bangalore²

2018

Interspeech 2018

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Conversational systems need to understand spoken language to be able to converse with a human in a meaningful coherent manner. This understanding (Spoken Language understanding-SLU) of the human language is operationalized through identifying intents and entities. While classification methods that rely on labeled data are often used for SLU, creating large supervised data sets is extremely tedious and time consuming. This paper presents a practical approach to automate the process of intent discovery on unlabeled data sets of human language text through clustering techniques. We explore a range of representations for the texts and various clustering methods to validate the clustering stability through quantitative metrics like Adjusted Random Index (ARI). A final alignment of the clusters to the semantic intent is determined through consensus labelling. Our experiments on public datasets demonstrate the effectiveness of our approach generating homogeneous clusters with 89% cluster accuracy, leading to better semantic intent alignments. Furthermore, we illustrate that the clustering offer an alternate and effective way to mine sentence variants that can aid the bootstrapping of SLU models.

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Section: Related Studymentioning

confidence: 99%

Intent Discovery Through Unsupervised Semantic Text Clustering

Padmasundari¹,

Bangalore²

2018

Interspeech 2018

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“…In addition to term expansion, utilization of contextual information of the short texts can be enhanced by machine translation (Tang et al, 2012). Direct clustering based on the continuous distributed representations of words, sentences, or paragraphs (Chinea-Rios et al, 2015;Mikolov et al, 2013) may also be worth of exploring. As a tradition in NLP research, further study will try all the promising combinations of the mentioned techniques to see which combinations perform best in which conditions.…”

Section: Demonstrationmentioning

confidence: 99%

Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration

Tseng

Lee

Chien

et al. 2018

Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

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Text clustering is a powerful technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectiveness. However, the lack of functions to process short text responses from the IRS prevents the further application of IRS in classes. Therefore, this study aims to propose a proper short text clustering module for the IRS, and demonstrate our implemented techniques through real-world examples, so as to provide experiences and insights for further study. In particular, we have compared three clustering methods and the result shows that theoretically better methods need not lead to better results, as there are various factors that may affect the final performance.

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Sentence Clustering Using Continuous Vector Space Representation

Cited by 2 publications

References 10 publications

Intent Discovery Through Unsupervised Semantic Text Clustering

Intent Discovery Through Unsupervised Semantic Text Clustering

Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration

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