Strings and Things: A Semantic Search Engine for news quotes using Named Entity Recognition

Emerging methods for content delivery such as quote-searching and entity-searching, enable users to quickly identify novel and relevant information from unstructured texts, news articles, and media sources. These methods have widespread applications in web surveillance and crime informatics, and can help improve intention disambiguation, character evaluation, threat analysis, and bias detection. Furthermore, quote-based and entity-based searching is also an empowering information retrieval tool that can enable non-technical users to gauge the quality of public discourse, allowing for more fine-grained analysis of core sociological questions. The paper presents a prototype search engine that allows users to search a news database containing quotes using a combination of strings and things. The ingestion pipeline, which forms the backend of the service, comprises of the following modules i) a crawler that ingests data from the GDELT Global Quotation Graph ii) a named entity recognition (NER) filter that labels data on the fly iii) an indexing mechanism that serves the data to an Elasticsearch cluster and iv) a user interface that allows users to formulate queries. The paper presents the high-level configuration of the pipeline and reports basic metrics and aggregations.