Representation Learning for Words and Entities

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.Comment: phd thesis, Machine Learning, Natural Language Processing, Representation Learning, Knowledge Graphs, Entities, Word Embeddings, Entity Embedding

Representation Learning for Words and Entities

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview LSA (MVLSA). Through experiments on close to 50 different views, I show that MVLSA outperforms other state-of-the-art word embedding models. After that, I focus on learning entity representations for search and recommendation and present the second algorithm of this thesis called Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. Moreover, I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints

Ranking and Retrieval under Semantic Relevance

This thesis presents a series of conceptual and empirical developments on the ranking and retrieval of candidates under semantic relevance. Part I of the thesis introduces the concept of uncertainty in various semantic tasks (such as recognizing textual entailment) in natural language processing, and the machine learning techniques commonly employed to model these semantic phenomena. A unified view of ranking and retrieval will be presented, and the trade-off between model expressiveness, performance, and scalability in model design will be discussed. Part II of the thesis focuses on applying these ranking and retrieval techniques to text: Chapter 3 examines the feasibility of ranking hypotheses given a premise with respect to a human's subjective probability of the hypothesis happening, effectively extending the traditional categorical task of natural language inference. Chapter 4 focuses on detecting situation frames for documents using ranking methods. Then we extend the ranking notion to retrieval, and develop both sparse (Chapter 5) and dense (Chapter 6) vector-based methods to facilitate scalable retrieval for potential answer paragraphs in question answering. Part III turns the focus to mentions and entities in text, while continuing the theme on ranking and retrieval: Chapter 7 discusses the ranking of fine-grained types that an entity mention could belong to, leading to state-of-the-art performance on hierarchical multi-label fine-grained entity typing. Chapter 8 extends the semantic relation of coreference to a cross-document setting, enabling models to retrieve from a large corpus, instead of in a single document, when resolving coreferent entity mentions