Topic models are a useful analysis tool to uncover the underlying themes within document collections. Probabilistic models which assume a generative story have been the dominant approach for topic modeling. We propose an alternative approach based on clustering readily available pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words. We provide benchmarks for the combination of different word embeddings and clustering algorithms, and analyse their performance under dimensionality reduction with PCA. The best performing combination for our approach is comparable to classical models, and complexity analysis indicate that this is a practical alternative to traditional topic modeling.
Analysis of online reviews has attracted great attention with broad applications. Often times, the textual reviews are coupled with the numerical ratings in the data. In this work, we propose a probabilistic model to accommodate both textual reviews and overall ratings with consideration of their intrinsic connection for a joint sentiment-topic prediction. The key of the proposed method is to develop a unified generative model where the topic modeling is constructed based on review texts and the sentiment prediction is obtained by combining review texts and overall ratings. The inference of model parameters are obtained by an efficient Gibbs sampling procedure. The proposed method can enhance the prediction accuracy of review data and achieve an effective detection of interpretable topics and sentiments. The merits of the proposed method are elaborated by the case study from Amazon datasets and simulation studies.
3D Garment modeling is a critical and challenging topic in the area of computer vision and graphics, with increasing attention focused on garment representation learning, garment reconstruction, and controllable garment manipulation, whereas existing methods were constrained to model garments under specific categories or with relatively simple topologies. In this paper, we propose a novel Neural Sewing Machine (NSM), a learning-based framework for structure-preserving 3D garment modeling, which is capable of learning representations for garments with diverse shapes and topologies and is successfully applied to 3D garment reconstruction and controllable manipulation. To model generic garments, we first obtain sewing pattern embedding via a unified sewing pattern encoding module, as the sewing pattern can accurately describe the intrinsic structure and the topology of the 3D garment. Then we use a 3D garment decoder to decode the sewing pattern embedding into a 3D garment using the UV-position maps with masks. To preserve the intrinsic structure of the predicted 3D garment, we introduce an inner-panel structure-preserving loss, an inter-panel structure-preserving loss, and a surface-normal loss in the learning process of our framework. We evaluate NSM on the public 3D garment dataset with sewing patterns with diverse garment shapes and categories. Extensive experiments demonstrate that the proposed NSM is capable of representing 3D garments under diverse garment shapes and topologies, realistically reconstructing 3D garments from 2D images with the preserved structure, and accurately manipulating the 3D garment categories, shapes, and topologies, outperforming the state-of-the-art methods by a clear margin.
Visualization and topic modeling are widely used approaches for text analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization space (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from text, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative model to jointly find topics and visualization, allowing the semantics to be infused in the visualization space for a meaningful interpretation. A major challenge that prevents these methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the first fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black box, it can handle model changes efficiently with little mathematical rederivation effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines.
This note is a short description of TeCoMiner, an interactive tool for exploring the topic content of text collections. Unlike other topic modeling tools, TeCoMiner is not based on some generative probabilistic model but on topological considerations about co-occurrence networks of terms. We outline the methods used for identifying topics, describe the features of the tool, and sketch an application, using a corpus of policy related scientific news on environmental issues published by the European Commission over the last decade.
Understanding the intent behind chat between customers and customer service agents has become a crucial problem nowadays due to an exponential increase in the use of the Internet by people from different cultures and educational backgrounds. More importantly, the explosion of e-commerce has led to a significant increase in text conversation between customers and agents. In this paper, we propose an approach to data mining the conversation intents behind the textual data. Using the customer service data set, we train unsupervised text representation models, and then develop an intent mapping model which would rank the predefined intents base on cosine similarity between sentences and intents. Topic-modeling techniques are used to define intents and domain experts are also involved to interpret topic modelling results. With this approach, we can get a good understanding of the user intentions behind the unlabelled customer service textual data.
Text summarization is the process of extracting the most important information from the text and presenting it concisely in fewer sentences. Call transcript is a text that involves textual description of a phone conversation between a customer (caller) and agent(s) (customer representatives). This paper presents an indigenously developed method that combines topic modeling and sentence selection with punctuation restoration in condensing ill-punctuated or un-punctuated call transcripts to produce summaries that are more readable. Extensive testing, evaluation and comparisons have demonstrated the efficacy of this summarizer for call transcript summarization.
The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e.g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective. To expand persuasiveness mining into a multi-modal realm, we present a multi-modal dataset, ImageArg, consisting of annotations of image persuasiveness in tweets. The annotations are based on a persuasion taxonomy we developed to explore image functionalities and the means of persuasion. We benchmark image persuasiveness tasks on ImageArg using widely-used multi-modal learning methods. The experimental results show that our dataset offers a useful resource for this rich and challenging topic, and there is ample room for modeling improvement.
Topic models such as LDA, DocNADE, iDocNADEe have been popular in document analysis. However, the traditional topic models have several limitations including: (1) Bag-of-words (BoW) assumption, where they ignore word ordering, (2) Data sparsity, where the application of topic models is challenging due to limited word co-occurrences, leading to incoherent topics and (3) No Continuous Learning framework for topic learning in lifelong fashion, exploiting historical knowledge (or latent topics) and minimizing catastrophic forgetting. This thesis focuses on addressing the above challenges within neural topic modeling framework. We propose: (1) Contextualized topic model that combines a topic and a language model and introduces linguistic structures (such as word ordering, syntactic and semantic features, etc.) in topic modeling, (2) A novel lifelong learning mechanism into neural topic modeling framework to demonstrate continuous learning in sequential document collections and minimizing catastrophic forgetting. Additionally, we perform a selective data augmentation to alleviate the need for complete historical corpora during data hallucination or replay.