Learning Document-Level Semantic Properties from Free-Text Annotations

This paper presents a new method for inferring the semantic properties of documents by leveraging free-text keyphrase annotations. Such annotations are becoming increasingly abundant due to the recent dramatic growth in semi-structured, user-generated online content. One especially relevant domain is product reviews, which are often annotated by their authors with pros/cons keyphrases such as ``a real bargain'' or ``good value.'' These annotations are representative of the underlying semantic properties; however, unlike expert annotations, they are noisy: lay authors may use different labels to denote the same property, and some labels may be missing. To learn using such noisy annotations, we find a hidden paraphrase structure which clusters the keyphrases. The paraphrase structure is linked with a latent topic model of the review texts, enabling the system to predict the properties of unannotated documents and to effectively aggregate the semantic properties of multiple reviews. Our approach is implemented as a hierarchical Bayesian model with joint inference. We find that joint inference increases the robustness of the keyphrase clustering and encourages the latent topics to correlate with semantically meaningful properties. Multiple evaluations demonstrate that our model substantially outperforms alternative approaches for summarizing single and multiple documents into a set of semantically salient keyphrases

Connecting Documents, Words, and Languages Using Topic Models

Topic models discover latent topics in documents and summarize documents at a high level. To improve topic models' topic quality and extrinsic performance, external knowledge is often incorporated as part of the generative story. One form of external knowledge is weighted text links that indicate similarity or relatedness between the connected objects. This dissertation 1) uncovers the latent structures in observed weighted links and integrates them into topic modeling, and 2) learns latent weighted links from other external knowledge to improve topic modeling. We consider incorporating links at three different levels: documents, words, and topics. We first look at binary document links, e.g., citation links of papers. Document links indicate topic similarity of the connected documents. Past methods model the document links separately, ignoring the entire link density. We instead uncover latent document blocks in which documents are densely connected and tend to talk about similar topics. We introduce LBH-RTM, a relational topic model with lexical weights, block priors, and hinge loss. It extracts informative topic priors from the document blocks for documents' topic generation. It predicts unseen document links with block and lexical features and hinge loss, in addition to topical features. It outperforms past methods in link prediction and gives more coherent topics. Like document links, words are also linked, but usually with real-valued weights. Word links are known as word associations and indicate the semantic relatedness of the connected words. They provide more information about word relationships in addition to the co-occurrence patterns in the training corpora. To extract and incorporate the knowledge in word associations, we introduce methods to find the most salient word pairs. The methods organize the words in a tree structure, which serves as a prior (i.e., tree prior) for tree LDA. The methods are straightforward but effective, yielding more coherent topics than vanilla LDA, and slightly improving the extrinsic classification performance. Weighted topic links are different. Topics are latent, so it is difficult to obtain ground-truth topic links, but learned weighted topic links could bridge the topics across languages. We introduce a multilingual topic model (MTM) that assumes each language has its own topic distributions over the words only in that language and learns weighted topic links based on word translations and words' topic distributions. It does not force the topic spaces of different languages to be aligned and is more robust than previous MTMs that do. It outperforms past MTMs in classification while still giving coherent topics on less comparable and smaller corpora

Hierarchical relational models for document networks

We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora

Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author's influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.Comment: Accepted by Transactions of the Association for Computational Linguistics (TACL); to appea