Probabilistic topic modeling provides a suite of tools for the unsupervised analysis of large collections of documents. Topic modeling algorithms can uncover the underlying themes of a collection and decompose its documents according to those themes. This analysis can be used for corpus exploration, document search, and a variety of prediction problems.In this tutorial, I will review the state-of-the-art in probabilistic topic models. I will describe the three components of topic modeling:(1) Topic modeling assumptions(2) Algorithms for computing with topic models(3) Applications of topic modelsIn (1), I will describe latent Dirichlet allocation (LDA), which is one of the simplest topic models, and then describe a variety of ways that we can build on it. These include dynamic topic models, correlated topic models, supervised topic models, author-topic models, bursty topic models, Bayesian nonparametric topic models, and others. I will also discuss some of the fundamental statistical ideas that are used in building topic models, such as distributions on the simplex, hierarchical Bayesian modeling, and models of mixed-membership.In (2), I will review how we compute with topic models. I will describe approximate posterior inference for directed graphical models using both sampling and variational inference, and I will discuss the practical issues and pitfalls in developing these algorithms for topic models. Finally, I will describe some of our most recent work on building algorithms that can scale to millions of documents and documents arriving in a stream.In (3), I will discuss applications of topic models. These include applications to images, music, social networks, and other data in which we hope to uncover hidden patterns. I will describe some of our recent work on adapting topic modeling algorithms to collaborative filtering, legislative modeling, and bibliometrics without citations.Finally, I will discuss some future directions and open research problems in topic models.

Probabilistic topic models

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together Multimodal machine learning aims to build models that can process and relate information from multiple modalities It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research

Multimodal Machine Learning: A Survey and Taxonomy

Latent variable models have the potential to add value to large document collections by discovering interpretable, low-dimensional subspaces. In order for people to use such models, however, they must trust them. Unfortunately, typical dimensionality reduction methods for text, such as latent Dirichlet allocation, often produce low-dimensional subspaces (topics) that are obviously flawed to human domain experts. The contributions of this paper are threefold: (1) An analysis of the ways in which topics can be flawed; (2) an automated evaluation metric for identifying such topics that does not rely on human annotators or reference collections outside the training data; (3) a novel statistical topic model based on this metric that significantly improves topic quality in a large-scale document collection from the National Institutes of Health (NIH).

/pdf/optimizing-semantic-coherence-in-topic-models-lp0a0ydrrv.pdf

Optimizing Semantic Coherence in Topic Models

The ability to associate images with natural language sentences that describe what is depicted in them is a hallmark of image understanding, and a prerequisite for applications such as sentence-based image search. In analogy to image search, we propose to frame sentence-based image annotation as the task of ranking a given pool of captions. We introduce a new benchmark collection for sentence-based image description and search, consisting of 8,000 images that are each paired with five different captions which provide clear descriptions of the salient entities and events. We introduce a number of systems that perform quite well on this task, even though they are only based on features that can be obtained with minimal supervision. Our results clearly indicate the importance of training on multiple captions per image, and of capturing syntactic (word order-based) and semantic features of these captions. We also perform an in-depth comparison of human and automatic evaluation metrics for this task, and propose strategies for collecting human judgments cheaply and on a very large scale, allowing us to augment our collection with additional relevance judgments of which captions describe which image. Our analysis shows that metrics that consider the ranked list of results for each query image or sentence are significantly more robust than metrics that are based on a single response per query. Moreover, our study suggests that the evaluation of ranking-based image description systems may be fully automated.

/pdf/framing-image-description-as-a-ranking-task-data-models-and-zvt55kzncz.pdf

Framing image description as a ranking task: data, models and evaluation metrics

The problem of joint modeling the text and image components of multimedia documents is studied. The text component is represented as a sample from a hidden topic model, learned with latent Dirichlet allocation, and images are represented as bags of visual (SIFT) features. Two hypotheses are investigated: that 1) there is a benefit to explicitly modeling correlations between the two components, and 2) this modeling is more effective in feature spaces with higher levels of abstraction. Correlations between the two components are learned with canonical correlation analysis. Abstraction is achieved by representing text and images at a more general, semantic level. The two hypotheses are studied in the context of the task of cross-modal document retrieval. This includes retrieving the text that most closely matches a query image, or retrieving the images that most closely match a query text. It is shown that accounting for cross-modal correlations and semantic abstraction both improve retrieval accuracy. The cross-modal model is also shown to outperform state-of-the-art image retrieval systems on a unimodal retrieval task.

/pdf/a-new-approach-to-cross-modal-multimedia-retrieval-1kvg795rcn.pdf

A new approach to cross-modal multimedia retrieval

The problem of cross-modal retrieval from multimedia repositories is considered. This problem addresses the design of retrieval systems that support queries across content modalities, for example, using an image to search for texts. A mathematical formulation is proposed, equating the design of cross-modal retrieval systems to that of isomorphic feature spaces for different content modalities. Two hypotheses are then investigated regarding the fundamental attributes of these spaces. The first is that low-level cross-modal correlations should be accounted for. The second is that the space should enable semantic abstraction. Three new solutions to the cross-modal retrieval problem are then derived from these hypotheses: correlation matching (CM), an unsupervised method which models cross-modal correlations, semantic matching (SM), a supervised technique that relies on semantic representation, and semantic correlation matching (SCM), which combines both. An extensive evaluation of retrieval performance is conducted to test the validity of the hypotheses. All approaches are shown successful for text retrieval in response to image queries and vice versa. It is concluded that both hypotheses hold, in a complementary form, although evidence in favor of the abstraction hypothesis is stronger than that for correlation.

/pdf/on-the-role-of-correlation-and-abstraction-in-cross-modal-2h511ihqqn.pdf

On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrieval

Many different topic models have been used successfully for a variety of applications However, even state-of-the-art topic models suffer from the important flaw that they do not capture the tendency of words to appear in bursts; it is a fundamental property of language that if a word is used once in a document, it is more likely to be used again We introduce a topic model that uses Dirichlet compound multinomial (DCM) distributions to model this burstiness phenomenon On both text and non-text datasets, the new model achieves better held-out likelihood than standard latent Dirichlet allocation (LDA) It is straightforward to incorporate the DCM extension into topic models that are more complex than LDA

/pdf/accounting-for-burstiness-in-topic-models-1ix5vqmmd8.pdf

Accounting for burstiness in topic models

We propose a Bayesian method of estimating a conditional distribution of data given metadata (e.g., the usage of a dialectal variant given a location) based on queries from a big data/social media source, such as Twitter. This distribution is structurally equivalent to those built from traditional experimental methods, despite lacking negative examples. Tests using Twitter to investigate the geographic distribution of dialectal forms show that this method can provide distributions that are tightly correlated with existing gold-standard studies at a fraction of the time, cost, and effort.

Mapping Dialectal Variation by Querying Social Media

This paper presents a syllable-based approach to unsupervised pattern discovery from speech. By first segmenting speech into syllable-like units, the system is able to limit potential word onsets and offsets to a finite number of candidate locations. These syllable tokens are then described using a set of features and clustered into a finite number of syllable classes. Finally, recurring syllable sequences or individual classes are treated as word candidates. Feasibility of the approach is investigated on spontaneous American English and Tsonga language samples with promising results. We also present a new and simple, oscillator-based algorithm for efficient unsupervised syllabic segmentation.

Gabriel Doyle

Papers

A new approach to cross-modal multimedia retrieval

On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrieval

Accounting for burstiness in topic models

Mapping Dialectal Variation by Querying Social Media

Unsupervised word discovery from speech using automatic segmentation into syllable-like units