59,596 research outputs found
Syntactic Topic Models
The syntactic topic model (STM) is a Bayesian nonparametric model of language
that discovers latent distributions of words (topics) that are both
semantically and syntactically coherent. The STM models dependency parsed
corpora where sentences are grouped into documents. It assumes that each word
is drawn from a latent topic chosen by combining document-level features and
the local syntactic context. Each document has a distribution over latent
topics, as in topic models, which provides the semantic consistency. Each
element in the dependency parse tree also has a distribution over the topics of
its children, as in latent-state syntax models, which provides the syntactic
consistency. These distributions are convolved so that the topic of each word
is likely under both its document and syntactic context. We derive a fast
posterior inference algorithm based on variational methods. We report
qualitative and quantitative studies on both synthetic data and hand-parsed
documents. We show that the STM is a more predictive model of language than
current models based only on syntax or only on topics
Modelling Grocery Retail Topic Distributions: Evaluation, Interpretability and Stability
Understanding the shopping motivations behind market baskets has high
commercial value in the grocery retail industry. Analyzing shopping
transactions demands techniques that can cope with the volume and
dimensionality of grocery transactional data while keeping interpretable
outcomes. Latent Dirichlet Allocation (LDA) provides a suitable framework to
process grocery transactions and to discover a broad representation of
customers' shopping motivations. However, summarizing the posterior
distribution of an LDA model is challenging, while individual LDA draws may not
be coherent and cannot capture topic uncertainty. Moreover, the evaluation of
LDA models is dominated by model-fit measures which may not adequately capture
the qualitative aspects such as interpretability and stability of topics.
In this paper, we introduce clustering methodology that post-processes
posterior LDA draws to summarise the entire posterior distribution and identify
semantic modes represented as recurrent topics. Our approach is an alternative
to standard label-switching techniques and provides a single posterior summary
set of topics, as well as associated measures of uncertainty. Furthermore, we
establish a more holistic definition for model evaluation, which assesses topic
models based not only on their likelihood but also on their coherence,
distinctiveness and stability. By means of a survey, we set thresholds for the
interpretation of topic coherence and topic similarity in the domain of grocery
retail data. We demonstrate that the selection of recurrent topics through our
clustering methodology not only improves model likelihood but also outperforms
the qualitative aspects of LDA such as interpretability and stability. We
illustrate our methods on an example from a large UK supermarket chain.Comment: 20 pages, 9 figure
Comparing Grounded Theory and Topic Modeling: Extreme Divergence or Unlikely Convergence?
Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method\u27s unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways
Ordering-sensitive and Semantic-aware Topic Modeling
Topic modeling of textual corpora is an important and challenging problem. In
most previous work, the "bag-of-words" assumption is usually made which ignores
the ordering of words. This assumption simplifies the computation, but it
unrealistically loses the ordering information and the semantic of words in the
context. In this paper, we present a Gaussian Mixture Neural Topic Model
(GMNTM) which incorporates both the ordering of words and the semantic meaning
of sentences into topic modeling. Specifically, we represent each topic as a
cluster of multi-dimensional vectors and embed the corpus into a collection of
vectors generated by the Gaussian mixture model. Each word is affected not only
by its topic, but also by the embedding vector of its surrounding words and the
context. The Gaussian mixture components and the topic of documents, sentences
and words can be learnt jointly. Extensive experiments show that our model can
learn better topics and more accurate word distributions for each topic.
Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM
obtains significantly better performance in terms of perplexity, retrieval
accuracy and classification accuracy.Comment: To appear in proceedings of AAAI 201
Dirichlet belief networks for topic structure learning
Recently, considerable research effort has been devoted to developing deep
architectures for topic models to learn topic structures. Although several deep
models have been proposed to learn better topic proportions of documents, how
to leverage the benefits of deep structures for learning word distributions of
topics has not yet been rigorously studied. Here we propose a new multi-layer
generative process on word distributions of topics, where each layer consists
of a set of topics and each topic is drawn from a mixture of the topics of the
layer above. As the topics in all layers can be directly interpreted by words,
the proposed model is able to discover interpretable topic hierarchies. As a
self-contained module, our model can be flexibly adapted to different kinds of
topic models to improve their modelling accuracy and interpretability.
Extensive experiments on text corpora demonstrate the advantages of the
proposed model.Comment: accepted in NIPS 201
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