In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable. Topic modeling is an effective technique for summarizing a collection of documents to find the main themes among them and to classify other documents that have a similar mixture of co-occurring words. We show how grounding a topic model with an ontology, extracted from a glossary of important domain phrases, improves the topics generated and makes them easier to understand. We apply and evaluate this method to the climate science domain. The result improves the topics generated and supports faster research understanding, discovery of social networks among researchers, and automatic ontology generation.
Early research explored ways of using word n-grams @cite_13 to improve different tasks such as text processing, classification, named entity recognition and knowledge base population. It is reasonable to believe word n-grams would produce better topics and research has shown this to be true. This idea of discovering word phrases in topic modeling was proposed by @cite_20 in 2007 and was based on using n-grams in topic models which was proposed by Wallach @cite_6 . Later work followed that proposed extensions to identifying topical phrases @cite_7 @cite_16 @cite_10 . Work by in 2013 @cite_2 combined n-gram models with temporal documents and was foundational in using ontological concepts to ground the topic modeling process.
@cite_7 @cite_10 @cite_6 @cite_2 @cite_16 @cite_13 @cite_20
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Standard statistical language models use n-grams to capture local dependencies, or use dynamic modeling techniques to track dependencies within an article. In this paper, we investigate a new statistical language model that captures topic-related dependencies of words within and across sentences. First, we develop a topic-dependent, sentence-level mixture language model which takes advantage of the topic constraints in a sentence or article. Second, we introduce topic-dependent dynamic adaptation techniques in the framework of the mixture model, using n-gram caches and content word unigram caches. Experiments with the static (or unadapted) mixture model on the North American Business (NAB) task show a 21 reduction in perplexity and a 3-4 improvement in recognition accuracy over a general n-gram model, giving a larger gain than that obtained with supervised dynamic cache modeling. Further experiments on the Switchboard corpus also showed a small improvement in performance with the sentence-level mixture model. Cache modeling techniques introduced in the mixture framework contributed a further 14 reduction in perplexity and a small improvement in recognition accuracy on the NAB task for both supervised and unsupervised adaptation. Most topic models, such as latent Dirichlet allocation, rely on the bag-of-words assumption. However, word order and phrases are often critical to capturing the meaning of text in many text mining tasks. This paper presents topical n-grams, a topic model that discovers topics as well as topical phrases. The probabilistic model generates words in their textual order by, for each word, first sampling a topic, then sampling its status as a unigram or bigram, and then sampling the word from a topic-specific unigram or bigram distribution. Thus our model can model "white house" as a special meaning phrase in the 'politics' topic, but not in the 'real estate' topic. Successive bigrams form longer phrases. We present experiments showing meaningful phrases and more interpretable topics from the NIPS data and improved information retrieval performance on a TREC collection. Character n-grams have been identified as the most successful feature in both singledomain and cross-domain Authorship Attribution (AA), but the reasons for their discriminative value were not fully understood. We identify subgroups of charactern-grams that correspond to linguistic aspects commonly claimed to be covered by these features: morphosyntax, thematic content and style. We evaluate the predictiveness of each of these groups in two AA settings: a single domain setting and a cross-domain setting where multiple topics are present. We demonstrate that characterngrams that capture information about affixes and punctuation account for almost all of the power of character n-grams as features. Our study contributes new insights into the use of n-grams for future AA work and other classification tasks. Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assumption, in which word order is ignored. Previously, these methods have not been combined. In this work, I explore a hierarchical generative probabilistic model that incorporates both n-gram statistics and latent topic variables by extending a unigram topic model to include properties of a hierarchical Dirichlet bigram language model. The model hyperparameters are inferred using a Gibbs EM algorithm. On two data sets, each of 150 documents, the new model exhibits better predictive accuracy than either a hierarchical Dirichlet bigram language model or a unigram topic model. Additionally, the inferred topics are less dominated by function words than are topics discovered using unigram statistics, potentially making them more meaningful.