Rule-based information extraction from text is increasingly being used to populate databases and to support structured queries on unstructured text. Specification of suitable information extraction rules requires considerable skill and standard practice is to refine rules iteratively, with substantial effort. In this paper, we show that techniques developed in the context of data provenance, to determine the lineage of a tuple in a database, can be leveraged to assist in rule refinement. Specifically, given a set of extraction rules and correct and incorrect extracted data, we have developed a technique to suggest a ranked list of rule modifications that an expert rule specifier can consider. We implemented our technique in the SystemT information extraction system developed at IBM Research -- Almaden and experimentally demonstrate its effectiveness.
Activation of transcription factors is a key driver event in cancer. We and others have recently reported that the Krüppel-like transcription factor KLF5 is activated in multiple epithelial cancer types including squamous cancer and gastrointestinal adenocarcinoma, yet the functional consequences and the underlying mechanisms of this activation remain largely unknown. Here we demonstrate that activation of KLF5 results in strongly selective KLF5 dependency for these cancer types. KLF5 bound lineage-specific regulatory elements and activated gene expression programs essential to cancer cells. HiChIP analysis revealed that multiple distal KLF5 binding events cluster and synergize to activate individual target genes. Immunoprecipitation-mass spectrometry assays showed that KLF5 interacts with other transcription factors such as TP63 and YAP1, as well as the CBP/EP300 acetyltransferase complex. Furthermore, KLF5 guided the CBP/EP300 complex to increase acetylation of H3K27, which in turn enhanced recruitment of the bromodomain protein BRD4 to chromatin. The 3D chromatin architecture aggregated KLF5-dependent BRD4 binding to activate polymerase II elongation at KLF5 target genes, which conferred a transcriptional vulnerability to proteolysis-targeting chimera–induced degradation of BRD4. Our study demonstrates that KLF5 plays an essential role in multiple epithelial cancers by activating cancer-related genes through 3D chromatin loops, providing an evidence-based rationale for targeting the KLF5 pathway. Significance: An integrative 3D genomics methodology delineates mechanisms underlying the function of KLF5 in multiple epithelial cancers and suggests potential strategies to target cancers with aberrantly activated KLF5.
Hand-crafted textual patterns have been the mainstay device of practical relation extraction for decades. However, there has been little work on reducing the manual effort involved in the discovery of effective textual patterns for relation extraction. In this paper, we propose a clustering-based approach to facilitate the pattern discovery for relation extraction. Specifically, we define the notion of semantic signature to represent the most salient features of a textual fragment. We then propose a novel clustering algorithm based on semantic signature, S2C, and its enhancement S2C+. Experiments on two real-world data sets show that, when compared with k-means clustering, S2C and S2C+ are at least an order of magnitude faster, while generating high quality clusters that are at least comparable to the best clusters generated by k-means without requiring any manual tuning. Finally, a user study confirms that our clustering-based approach can indeed help users discover effective textual patterns for relation extraction with only a fraction of the manual effort required by the conventional approach.
The primary goal of the Midas project is to build a system that enables easy and scalable integration of unstructured and semi-structured information present across multiple data sources. As a first step in this direction, we have built a system that extracts and integrates information from regulatory filings submitted to the U.S. Securities and Exchange Commission (SEC) and the Federal Deposit Insurance Corporation (FDIC). Midas creates a repository of entities, events, and relationships by extracting, conceptualizing, integrating, and aggregating data from unstructured and semi-structured documents. This repository enables applications to use the extracted and integrated data in a variety of ways including mashups with other public data and complex risk analysis.
Information Extraction (IE) -the problem of extracting structured information from unstructured text -has become the key enabler for many enterprise applications such as semantic search, business analytics and regulatory compliance. While rule-based IE systems are widely used in practice due to their well-known "explainability," developing high-quality information extraction rules is known to be a labor-intensive and time-consuming iterative process.Our demonstration showcases SystemT IDE, the integrated development environment for SystemT, a state-of-the-art rulebased IE system from IBM Research that has been successfully embedded in multiple IBM enterprise products. Sys-temT IDE facilitates the development, test and analysis of high-quality IE rules by means of sophisticated techniques, ranging from data management to machine learning. We show how to build high-quality IE annotators using a suite of tools provided by SystemT IDE, including computing data provenance, learning basic features such as regular expressions and dictionaries, and automatically refining rules based on labeled examples.
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