Word Topic Models for Spoken Document Retrieval and Transcription

Wang

IEICE Trans. Inf. & Syst.

2012

Self Cite

SUMMARYThis paper describes the application of two attractive categories of topic modeling techniques to the problem of spoken document retrieval (SDR), viz. document topic model (DTM) and word topic model (WTM). Apart from using the conventional unsupervised training strategy, we explore a supervised training strategy for estimating these topic models, imagining a scenario that user query logs along with click-through information of relevant documents can be utilized to build an SDR system. This attempt has the potential to associate relevant documents with queries even if they do not share any of the query words, thereby improving on retrieval quality over the baseline system. Likewise, we also study a novel use of pseudo-supervised training to associate relevant documents with queries through a pseudo-feedback procedure. Moreover, in order to lessen SDR performance degradation caused by imperfect speech recognition, we investigate leveraging different levels of index features for topic modeling, including words, syllable-level units, and their combination. We provide a series of experiments conducted on the TDT (TDT-2 and TDT-3) Chinese SDR collections. The empirical results show that the methods deduced from our proposed modeling framework are very effective when compared with a few existing retrieval approaches. key words : spoken document retrieval, topic model, supervised training, pseudo-supervised training, subword-level indexing

Section: Word Topic Model (Wtm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spoken Document Retrieval Leveraging Unsupervised and Supervised Topic Modeling Techniques

Wang

IEICE Trans. Inf. & Syst.

2012

Self Cite

“…They have been employed for tackling text mining problems including document classification (Jameel and Lam 2013b;Rubin et al 2012;Li et al 2015) and document retrieval (Wei and Croft 2006;Wang et al 2007;Chen 2009;Yi and Allan 2009;Egozi et al 2011;Andrzejewski and Buttler 2011;Wang et al 2011Wang et al , 2013aLu et al 2011;Yi and Allan 2008;Cao et al 2007a;Park and Ramamohanarao 2009;Duan et al 2012). These models can achieve better performance via detecting the latent topic structure and establishing a relationship between the latent topic and the goal of the problem.…”

Section: Introductionmentioning

confidence: 99%

Supervised topic models with word order structure for document classification and retrieval learning

Jameel

Lam

2015

Inf Retrieval J

One limitation of most existing probabilistic latent topic models for document classification is that the topic model itself does not consider useful side-information, namely, class labels of documents. Topic models, which in turn consider the side-information, popularly known as supervised topic models, do not consider the word order structure in documents. One of the motivations behind considering the word order structure is to capture the semantic fabric of the document. We investigate a low-dimensional latent topic model for document classification. Class label information and word order structure are integrated into a supervised topic model enabling a more effective interaction among such information for solving document classification. We derive a collapsed Gibbs sampler for our model. Likewise, supervised topic models with word order structure have not been explored in document retrieval learning. We propose a novel supervised topic model for document retrieval learning which can be regarded as a pointwise model for tackling the learning-to-rank task. Available relevance assessments and word order structure are integrated into the topic model itself. We conduct extensive experiments on several publicly available benchmark datasets, and show that our model improves upon the state-of-the-art models.

Proceedings of the 15th International Conference on Intelligent User Interfaces

“…It has been applied unimodally to text or transcribed speech for language modeling [15], document clustering [16], spoken document retrieval [17], document summarization [18], etc. The objective of our current work is to apply LSM in capturing the latent semantics of the multimodal user inputs.…”

Section: Introductionmentioning

confidence: 99%

Usage patterns and latent semantic analyses for task goal inference of multimodal user interactions

Hui

Meng

2010

This paper describes our work in usage pattern analysis and development of a latent semantic analysis framework for interpreting multimodal user input consisting speech and pen gestures.We have designed and collected a multimodal corpus of navigational inquiries. Each modality carries semantics related to domain-specific task goal. Each inquiry is annotated manually with a task goal based on the semantics. Multimodal input usually has a simpler syntactic structure than unimodal input and the order of semantic constituents is different in multimodal and unimodal inputs. Therefore, we proposed to use semantic analysis to derive the latent semantics from the multimodal inputs using latent semantic modeling (LSM). In order to achieve this, we parse the recognized Chinese spoken input for the spoken locative references (SLR). These SLRs are then aligned with their corresponding pen gesture(s). Then, we characterized the cross-modal integration pattern as 3-tuple multimodal terms with SLR, pen gesture type and their temporal relation. The inquiry-multimodal term matrix is then decomposed using singular value decomposition (SVD) to derive the latent semantics automatically. Task goal inference based on the latent semantics shows that the task goal inference accuracy on a disjoint test set is of 99%.