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

Weighting Finite-State Transductions With Neural Context

How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the out- put string, using recurrent neural networks. In contrast, we propose to keep the traditional architecture, which uses a finite-state trans- ducer to score all possible output strings, but to augment the scoring function with the help of recurrent networks. A stack of bidirec- tional LSTMs reads the input string from left- to-right and right-to-left, in order to summa- rize the input context in which a transducer arc is applied. We combine these learned fea- tures with the transducer to define a probabil- ity distribution over aligned output strings, in the form of a weighted finite-state automaton. This reduces hand-engineering of features, al- lows learned features to examine unbounded context in the input string, and still permits ex- act inference through dynamic programming. We illustrate our method on the tasks of mor- phological reinflection and lemmatization

Script Induction as Language Modeling

The narrative cloze is an evaluation met-ric commonly used for work on automatic script induction. While prior work in this area has focused on count-based meth-ods from distributional semantics, such as pointwise mutual information, we argue that the narrative cloze can be productively reframed as a language modeling task. By training a discriminative language model for this task, we attain improvements of up to 27 percent over prior methods on stan-dard narrative cloze metrics.