People write everyday — articles, blogs, emails — with a purpose and an audience in mind. Politicians adapt their speeches to convince their audience, news media slant stories for their market, and teenagers on social media seek social status among their peers through their posts. Hence, language is purposeful and strategic. In this thesis, we introduce a framework for text analysis that make explicit the purposefulness of the author and develop methods that consider the interaction between the author, her text, and her audience's responses. We frame the authoring process as a decision theoretic problem — the observed text is the result of an author maximizing her utility.

We will explore this perspective by developing a set of novel statistical models that characterize authors' strategic behaviors through their utility functions. We consider three particular domains — political campaigns, the scientific community, and the judiciary — using our models and develop the necessary tools to evaluate our assumptions and hypotheses. In each of these domains, our models yield better response prediction accuracy and provide an interpretable means of investigating the underlying processes. Together, they exemplify our approach to text modeling and data exploration.

Throughout this thesis, we will illustrate how our models can be used as tools for in-depth exploration of text data and hypothesis generation.

Thesis Committee:
Noah Smith (Chair)
Eduard Hovy
Daniel Neill (Heinz/CMU)
Jing Jiang (Singapore Management University)
Philip Resnik (University of Maryland, College Park)

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Retrieval pipelines commonly rely on a term-based search to obtain candidate records,  which are subsequently re-ranked. Some candidates  are missed by this approach,  e.g., due to a vocabulary mismatch.  We address this issue by replacing  the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle word similarities, computed via IBM Model 1.

To implement this idea,  we first develop an extendible framework (NMSLIB) for searching in generic, not necessarily metric, spaces. Second, we implement new and existing search methods within  NMSLIB to carry out an extensive evaluation. Third,  we employ k-NN methods as a replacement for term-based retrieval in an ad hoc  retrieval task.

While an exact brute-force k-NN search using the similarity function  incorporating IBM Model 1 is  slow, we demonstrate that   an   approximate    algorithm   is  nearly two orders of magnitude  faster at the expense  of only  a small loss in accuracy.  A retrieval pipeline relying on an approximate k-NN search can be more effective than   the  term-based  pipeline. This opens up new possibilities for designing effective retrieval pipelines.

Thesis Committee:
Eric Nyerg (Chair)
Jamie Callan
Alex Hauptmann
James Allan (University of Massachusetts, Amherst)

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Selective search is a modern distributed search architecture designed to reduce the computational cost of large-scale search. Prior work has shown selective search to be effective in smaller, single query-at-a-time environments. This thesis aims to bring selective search to wider adoption by addressing the questions related to efficiency and effectiveness in a practical implementation.

The first set of investigations relate to efficiency. A practical implementation of selective search would use a parallel query processing environment to maximize throughput but it is unknown whether selective search would retain is efficiency advantage over exhaustive search in this environment. This proposal presents load-balancing solutions to manage unequal popularity of shards and balance the cost of resource selection. Overall, with proper load management, the efficiency claims of prior work remain relevant in a parallel processing setting and larger computational environments. Production systems also use query optimization techniques such as dynamic pruning. In this work, selective search was combined with WAND, a common dynamic pruning algorithm and it was shown that selective search and WAND had better-than-additive gains due to the long posting lists of the topically focused shards.

The second half of the proposal research addresses effectiveness. Prior work has shown that a single instance of selective search has reasonable accuracy. However, this selective search system contained non-deterministic steps. This thesis investigated the effects of random decisions on the accuracy of selective search and found the variance across system instances is acceptable.

During the course of the thesis research, it was found that resource selection remains a major source of errors in a selective search system. Thus, a new resource selection algorithm was explored, using the pre-existing statistics collected by Block-Max WAND. We found that in a single-term query, the new method proved more effective than existing methods and comparable to exhaustive search.

Thesis Committee:
Jamie Callan (Chair)
Jaime Carbonell
Ralf Brown
Alistair Moffat (University of Melbourne)

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Developing NLP tools for many languages involves unique challenges not typically encountered in English NLP work (e.g., limited annotations, unscalable architectures, code switching). Although each language is unique, different languages often exhibit similar characteristics (e.g., phonetic, morphological, lexical, syntactic) which can be exploited to synergistically train analyzers for multiple languages. In this thesis, we advocate for a novel language-universal approach to multilingual NLP in which one statistical model trained on multilingual, homogenuous annotations is used to process natural language input in multiple languages.

To empirically show the merits of the proposed approach, we develop MALOPA, a language-unidersal dependency parser which outperforms monolingually-trained parsers in several low-resource and high-resource scenarios.  MALOPA us a greedy transition-based parser which uses multilingual word embeddings and other language-universal efeatures as a homogeneous representation of the imput across all languages. To address the syntactice differences between languages, MALOPA makes use of token-level language information as well as language0specific representations such as fine-grained part-of-speech tags. MALOPA uses a recrurrent neural network architecture and multitask learning to jointly predict POS tags and labels dependency parses.

Focusing on homogeneous input representations, we propose novel methods for stimating multilingual word embeddings and for predicting word alignments. We develop two methods for estimating multilingual word embeddings from bilingual dictionaries and monolingual corpora. The first estimation method, multiCluster, learns embeddings of word clusters which may contain words from different languages, and learns distributional similarities by pooling the contexts of all words in the same cluster in multiple monolingual corpora. The second estimation method, multiCCA, learns a linear projection of monolingually trained embeddings in each language to one vector space, extending the work of Faruqui and Dyer (2014) to more than two languages. To show the scalability of our methods, we train multilingual embeddings in 59 languages. We also develop an extensible, easy-to-use web-based evaluation portal for evaluating arbitrary multilingual word embeddings on several intrinsic and extrinsic tasks. We develop the conditional random field autoencoder (CRF autoencoder) model for un supervised learning of structured predictors, and use it to predict word alignments in parallel corpora. We use a feature-rich CRF model to predict the latent representation conditional on the observed input, then reconstruct the input conditional on the latent representation using a generative model which factorizes similarly to the CRF model. To reconstruct the observations, we experiment with a categorical distribution over word types (or word clusters), and a multivariate Gaussian distribution that generates pretrained word embeddings

Thesis Committee:
Chris Dyer (Co-Chair)
Noah Smith (Co-Chair, University of Washington)
Tom Mitchell
Kuzman Ganchev (Google Research)

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Scientific discovery was long viewed as a uniquely human creative activity, but digital computers can now reproduce many facets of this process. In this talk, I review the history of research on computational systems that discover scientific knowledge. The general framework posits that discovery involves search through a space of hypotheses, laws, or models, and that this search is guided both by domain knowledge and by regularities in data. Next I turn to one paradigm -- inductive process modeling -- that encodes models as sets of processes incorporating differential equations, induces these models from observational data, and uses background knowledge to aid in their construction. I illustrate the operation of implemented systems on data sets from ecology and environmental science, showing they produce accurate and interpretable models. I also report an improved framework that, by adopting a few simplifying assumptions, reliably produces more accurate fits and scales far better to complex models, along with recent work on adapting models to altered settings. In closing, I discuss challenges for research on scientific discovery and their role in the e-science movement, which uses computational methods to understand and support the scientific enterprise.

This talk describes joint work with Kevin Arrigo, Adam Arvay, Stuart Borrett, Will Bridewell, Ljupco Todorovski, and others. Papers are available.

Dr. Pat Langley serves as Director of the Institute for the Study of Learning and Expertise and as Professor of Computer Science at the University of Auckland. He has contributed to artificial intelligence and cognitive science for 35 years, he was founding Executive Editor of Machine Learning, and he is currently Editor for Advances in Cognitive Systems. His current research focuses on induction of explanatory scientific models and on architectures for interactive intelligent agents.

The successes of information retrieval in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems.

This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our methods, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity
This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines words and entities together with their uncertainties considered.  At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations.

Thesis Committee:
Jamie Callan (Chair)
William Cohen
Tie-Yan Liu  (CMU/Microsoft Research)
Bruce Croft (University of Massachusetts, Amherst)

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The automated analysis of video data becomes ever more important as we are inundated by the ocean of videos generated every day. Current state-of-the-art algorithms in these tasks are mainly supervised, i.e. the algorithms learn models based on manually labeled training data. However, large quantities of high quality labeled data are very difficult to collect manually.Therefore, in this thesis, we propose to circumvent this problem by automatically harvesting and exploiting useful information from unlabeled videos based on 1) out-of-domain external knowledge sources and 2) internal constraints in video.

Two surveillance video analysis tasks were targeted: multi-object tracking and pose estimation. For multi-object tracking, we leveraged an external knowledge source: face recognition, to perform identity-aware person localization and tracking. We also utilized an internal constraint: spatial-temporal smoothness, to automatically collect person re-identification training data and learn deep appearance features, which further enhanced tracking performance. For pose estimation, we exploited the spatial-temporal smoothness constraint in a self-training framework to perform unsupervised domain adaptation. Finally, the proposed algorithms were used to analyze a nursing home data set which consisted of thousands of hours of surveillance video.

Our experimental results showed that the external knowledge and internal constraints utilized were effective in collecting useful information from unlabeled videos to enhance multi-object tracking and pose estimation performance. Based on the promising experimental results, we believe that other video analysis problems could also benefit from utilizing external knowledge or internal constraints in an unsupervised manner, thus reducing the need to manually label data. Furthermore, our proposed methods potentially open the door to automated analysis on the ocean of surveillance video generated every day.

Thesis Committee:
Alexander Hauptmann (Chair)
Abhinav Gupta
Yaser Sheikh
Rahul Sukthankar (Google Research)

Copy of Thesis Document

The performance of automatic speech recognition (ASR) has improved tremendously due to the application of deep neural networks (DNNs). Despite this progress, building a new ASR system remains a challenging task, requiring various resources, multiple training stages and significant expertise. In this talk, I will present an approach that drastically simplifies building acoustic models for the existing weighted finite state transducer (WFST) based decoding approach, and lends itself to end-to-end speech recognition, allowing optimization for arbitrary criteria. Acoustic modeling now involves learning a single recurrent neural network (RNN), which predicts context-independent targets (e.g., syllables, phonemes or characters). The connectionist temporal classification (CTC) objective function marginalizes over all possible alignments between speech frames and label sequences, removing the need for a separate alignment of the training data. We present a generalized decoding approach based on weighted finite-state transducers (WFSTs), which enables the efficient incorporation of lexicons and language models into CTC decoding. Experiments show that this approach achieves state-of-the-art word error rates, while drastically reducing complexity and speeding up decoding when compared to standard hybrid DNN systems.

Florian Metze is an Associate Research Professor at Carnegie Mellon’s University, in the Language Technologies Institute. His work is centered around speech and multi-media processing with a focus on low resource and multi-lingual speech processing, large-scale multi-media retrieval and summarization, along with recognition of personality or similar meta-data from speech.

Social media such as Twitter and Facebook provide insights into the personalities and concerns of people and communities.  We analyze tens of millions of Facebook posts and billions of tweets to study variation in language use with age, gender, personality, and mental and physical well-being.  Word clouds visually illustrate the big five personality traits (e.g., "What is it like to be neurotic?"), while correlations between language use and health data at both the individual and county level suggest connections between health, happiness, personality and culture, including potential psychological causes of heart disease.     


Dr. Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he also holds appointments in multiple departments in the Schools of Business, Medicine, Arts and Sciences, and Engineering and Applied Science. Lyle received a B.S. from Stanford University and a Ph.D. from M.I.T. He has published over 200 articles and in co-inventor on eleven patents. His current research focuses on developing scalable machine learning methods for data mining and text mining, including spectral methods for natural language processing, statistical models for aggregating crowd-sourced predictions, and techniques to analyze social media to better understand the drivers of physical and mental well-being.

Faculty Host: Jaime Carbonell


Learning to reason and understand the world’s knowledge is a fundamental problem in Artificial Intelligence (AI). While it is always hypothesized that both the symbolic and statistical approaches are necessary to tackle complex problems in AI, in practice, bridging the two in a combined framework might bring intractability—most probabilistic first-order logics are simply not efficient enough for real-world sized tasks.

With the vast amount of relational data available in digital form, now is a good opportunity to close the gap between these two paradigms. The core research question that I will address in this thesis defense is the following: how can we design scalable statistical learning and inference methods to operate over rich knowledge representations? I will describe some examples of my work in advancing the state-of-the-arts in theories and practices of statistical relational learning, including: 1) ProPPR, a scalable learning and reasoning framework whose inference time does not depend on the size of knowledge graphs; 2) an efficient structural gradient based meta-reasoning approach that learns formulas from relational data, and a soft version of predicate invention, and representation learning for logic 3) and an application of joint information extraction and relational reasoning in NLP. I will conclude this talk by describing my future research plans.

Thesis Committee:
William Cohen (Chair)
Tom Mitchell
Christos Faloutsos
Eric Horvitz (Microsoft Research)

Copy of Thesis Document


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