174 research outputs found
A comparative study of Bayesian models for unsupervised sentiment detection
This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentimenttopic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning
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Review of computer vision in intelligent environment design
This paper discusses and compares the use of vision based and non-vision based technologies in developing intelligent environments. By reviewing the related projects that use vision based techniques in intelligent environment design, the achieved functions, technical issues and drawbacks of those projects are discussed and summarized, and the potential solutions for future improvement are proposed, which leads to the prospective direction of my PhD research
Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin
Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation
Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a "top'' (e.g. a shirt) and a "bottom'' (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain
Improving Medical Dialogue Generation with Abstract Meaning Representations
Medical Dialogue Generation serves a critical role in telemedicine by
facilitating the dissemination of medical expertise to patients. Existing
studies focus on incorporating textual representations, which have limited
their ability to represent the semantics of text, such as ignoring important
medical entities. To enhance the model's understanding of the textual semantics
and the medical knowledge including entities and relations, we introduce the
use of Abstract Meaning Representations (AMR) to construct graphical
representations that delineate the roles of language constituents and medical
entities within the dialogues. In this paper, We propose a novel framework that
models dialogues between patients and healthcare professionals using AMR
graphs, where the neural networks incorporate textual and graphical knowledge
with a dual attention mechanism. Experimental results show that our framework
outperforms strong baseline models in medical dialogue generation,
demonstrating the effectiveness of AMR graphs in enhancing the representations
of medical knowledge and logical relationships. Furthermore, to support future
research in this domain, we provide the corresponding source code at
https://github.com/Bernard-Yang/MedDiaAMR.Comment: Submitted to ICASSP 202
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