Quantifying the coherence of a set of statements is a long standing problem with many potential applications that has attracted researchers from different sciences. The special case of measuring coherence of topics has been recently studied to remedy the problem that topic models give no guaranty on the interpretablity of their output. Several benchmark datasets were produced that record human judgements of the interpretability of topics. We are the first to propose a framework that allows to construct existing word based coherence measures as well as new ones by combining elementary components. We conduct a systematic search of the space of coherence measures using all publicly available topic relevance data for the evaluation. Our results show that new combinations of components outperform existing measures with respect to correlation to human ratings. nFinally, we outline how our results can be transferred to further applications in the context of text mining, information retrieval and the world wide web.

Exploring the Space of Topic Coherence Measures

Collection and especially analysis of open-ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author's gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments.

/pdf/structural-topic-models-for-open-ended-survey-responses-273f1skepv.pdf

Structural topic models for open ended survey responses

Uncovering the topics within short texts, such as tweets and instant messages, has become an important task for many content analysis applications. However, directly applying conventional topic models (e.g. LDA and PLSA) on such short texts may not work well. The fundamental reason lies in that conventional topic models implicitly capture the document-level word co-occurrence patterns to reveal topics, and thus suffer from the severe data sparsity in short documents. In this paper, we propose a novel way for modeling topics in short texts, referred as biterm topic model (BTM). Specifically, in BTM we learn the topics by directly modeling the generation of word co-occurrence patterns (i.e. biterms) in the whole corpus. The major advantages of BTM are that 1) BTM explicitly models the word co-occurrence patterns to enhance the topic learning; and 2) BTM uses the aggregated patterns in the whole corpus for learning topics to solve the problem of sparse word co-occurrence patterns at document-level. We carry out extensive experiments on real-world short text collections. The results demonstrate that our approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics. Furthermore, we find that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model.

http://www.bigdatalab.ac.cn/~lanyanyan/papers/2013/WWW2013-yan.pdf

A biterm topic model for short texts

We present LDAvis, a web-based interactive visualization of topics estimated using Latent Dirichlet Allocation that is built using a combination of R and D3. Our visualization provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. First, we propose a novel method for choosing which terms to present to a user to aid in the task of topic interpretation, in which we define the relevance of a term to a topic. Second, we present results from a user study that suggest that ranking terms purely by their probability under a topic is suboptimal for topic interpretation. Last, we describe LDAvis, our visualization system that allows users to flexibly explore topic-term relationships using relevance to better understand a fitted LDA model.

/pdf/ldavis-a-method-for-visualizing-and-interpreting-topics-293hxgmxqn.pdf

LDAvis: A method for visualizing and interpreting topics

This paper demonstrates how to use the R package stm for structural topic modeling. The structural topic model allows researchers to flexibly estimate a topic model that includes document-level metadata. Estimation is accomplished through a fast variational approximation. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest.

/pdf/stm-an-r-package-for-structural-topic-models-3vfz80akjf.pdf

stm: An R Package for Structural Topic Models

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

Investigation of synaptic vesicle membrane proteins using multidisciplinary approaches, particularly to characterize synaptic vesicle proteins in synapses, can greatly advance our knowledge of the molecular mechanisms involved in synaptic vesicle exocytosis and neurotransmission. Three approaches are presented in this unit to identify and characterize synaptic vesicle proteins. The first is a subcellular fractionation method used to isolate synaptic vesicles from rat brain synaptosomal preparations, which can then be used in a variety of biochemical studies on synaptic vesicle proteins. The second is a detailed procedure for pre-embedding immunogold staining and electron microscopic observation, techniques that permit the morphological identification of synaptic vesicle proteins in individual vesicles at the intact synapse. Finally, a protocol for immunocytochemical staining of cultured hippocampal neurons for light microscopic examination is provided, which allows one to stain multisynaptic vesicle proteins and determine their localization in relation to other proteins or subcellular structures in synapses.


Keywords:

Synaptosomes;
hippocampal neurons;
synapses;
light-microscopy;
electron-microscopy;
immunocytochemistry;
immunoblotting

Multidisciplinary Approaches for Characterizing Synaptic Vesicle Proteins

1 Division of Neurology, Children’s Hospital of Philadelphia, and Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA 2 National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA 3 Epilepsy Genetics Program, Department of Neurology, Boston Children’s Hospital, Boston, MA, USA *Correspondence: Eric D. Marsh, Neurology and Pediatrics, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104-4399, USA. Email: MARSHE@email.chop.edu

https://journals.sagepub.com/doi/pdf/10.1177/15357597211023712

Miriam Leenders

Papers

Optimizing Semantic Coherence in Topic Models

Multidisciplinary Approaches for Characterizing Synaptic Vesicle Proteins

The 2021 Epilepsy Research Benchmarks—Respecting Core Principles, Reflecting Evolving Community Priorities: