8 research outputs found
Association Graph Learning for Multi-Task Classification with Category Shifts
In this paper, we focus on multi-task classification, where related
classification tasks share the same label space and are learned simultaneously.
In particular, we tackle a new setting, which is more realistic than currently
addressed in the literature, where categories shift from training to test data.
Hence, individual tasks do not contain complete training data for the
categories in the test set. To generalize to such test data, it is crucial for
individual tasks to leverage knowledge from related tasks. To this end, we
propose learning an association graph to transfer knowledge among tasks for
missing classes. We construct the association graph with nodes representing
tasks, classes and instances, and encode the relationships among the nodes in
the edges to guide their mutual knowledge transfer. By message passing on the
association graph, our model enhances the categorical information of each
instance, making it more discriminative. To avoid spurious correlations between
task and class nodes in the graph, we introduce an assignment entropy
maximization that encourages each class node to balance its edge weights. This
enables all tasks to fully utilize the categorical information from related
tasks. An extensive evaluation on three general benchmarks and a medical
dataset for skin lesion classification reveals that our method consistently
performs better than representative baselines
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