Joint Models for Information and Knowledge Extraction

for question answering, semantic search, automatic summarization, and other machine reading
applications. There are many sub-tasks involved such as named entity recognition, named entity disambiguation,
co-reference resolution, relation extraction, event detection, discourse parsing, and others. Solving these tasks is
challenging as natural language text is unstructured, noisy, and ambiguous. Key challenges, which focus on identifying
and linking named entities, as well as discovering relations between them, include:
• High NERD Quality. Named entity recognition and disambiguation, NERD for short, are preformed first in the
extraction pipeline. Their results may affect other downstream tasks.
• Coverage vs. Quality of Relation Extraction. Model-based information extraction methods achieve high extraction
quality at low coverage, whereas open information extraction methods capture relational phrases between entities.
However, the latter degrades in quality by non-canonicalized and noisy output. These limitations need to be overcome.
• On-the-fly Knowledge Acquisition. Real-world applications such as question answering, monitoring content streams, etc.
demand on-the-fly knowledge acquisition. Building such an end-to-end system is challenging because it requires high
throughput, high extraction quality, and high coverage.
This dissertation addresses the above challenges, developing new methods to advance the state of the art. The first
contribution is a robust model for joint inference between entity recognition and disambiguation. The second
contribution is a novel model for relation extraction and entity disambiguation on Wikipedia-style text. The third
contribution is an end-to-end system for constructing query-driven, on-the-fly knowledge bases.