The Future of Search EnginesSandlin, Anu  | Aug 31, 2018
Search engines have changed the world. They put vast amounts of information at our fingertips. But search engines have their flaws, says iSchool Associate Professor Matthew Lease. Search results are often not as “smart” as we’d like them to be, lacking a true understanding of language and human logic. They can also replicate and deepen the biases embedded in our searches, rather than bringing us new information or insight.
Dr. Lease believes there may be better ways to harness the dual power of computers and human minds to create more intelligent information retrieval (IR) systems, benefiting general search engines, as well as niche ones like those used for medical knowledge or non-English texts. At the 2017 Annual Meeting of the Association for Computational Linguistics in Vancouver, Canada, Dr. Lease and his collaborators from The University of Texas at Austin and Northeastern University presented two papers describing their novel information retrieval systems using research that leverages the supercomputing resources at UT Austin’s Texas Advanced Computer Center.
In one paper, they presented a method that combines input from multiple annotators—humans who hand-label data used to train and evaluate intelligent algorithms—to determine the best overall annotation for a given text. They applied this method to two problems. First, they analyzed free-text research articles describing medical studies to extract details of each study, such as patient condition, demographics, treatments, and outcomes. They also used name-entity recognition to analyze breaking news stories to identify the events, people, and places involved.
“An important challenge in natural language processing is accurately finding important information contained in free-text, which lets us extract it into databases and combine it with other data to make more intelligent decisions and new discoveries,” Dr. Lease said. “We’ve been using crowdsourcing to annotate medical and news articles at scale so that our intelligent systems will be able to more accurately find the key information contained in each article.”
An important challenge in natural language processing is accurately finding important information contained in free-text, which lets us extract it into databases and combine it with other data to make more intelligent decisions and new discoveries
Such annotation has traditionally been performed by in-house, domain experts. However, crowdsourcing has recently become a popular means to acquire large, labeled datasets at lower cost. Predictably, annotations from laypeople are of lower quality than those from domain experts, so it is necessary to estimate the reliability of crowd annotators, and also aggregate individual annotations to come up with a single set of “reference standard” consensus labels.
Lease’s team found that their method was able to train a neural network—a form of artificial intelligence (AI) modeled on the human brain—so it could very accurately predict named entities and extract relevant information in unannotated texts. The new method improves upon existing tagging and training methods. It also provides an estimate of each worker’s label quality, which can be transferred between tasks and is useful for error analysis and intelligently routing tasks—identifying the best person to annotate each particular text.
The group’s second paper addressed the fact that neural models for natural language processing (NLP) often ignore existing resources like WordNet—a lexical database for the English language that groups words into sets of synonyms—or domain-specific ontologies, such as the Unified Medical Language System, which encode knowledge about a given field.
They proposed a method for exploiting these existing linguistic resources via weight sharing to improve NLP models for automatic text classification. For example, their model learns to classify whether or not published medical articles describing clinical trials are relevant to a well-specified clinical question. In weight sharing, similar words share some fraction of a weight, or assigned numerical value. Weight sharing constrains the number of free parameters that a system must learn, thereby increasing the efficiency and accuracy of the neural model, and serving as a flexible way to incorporate prior knowledge. In doing so, they combine the best of human knowledge with machine learning.
“Neural network models have tons of parameters and need lots of data to fit them,” said Lease. “We had this idea that if you could somehow reason about some words being related to other words a priori, then instead of having to have a parameter for each one of those word separately, you could tie together the parameters across multiple words and in that way, need less data to learn the model. It would realize the benefits of deep learning without large data constraints.”
They applied a form of weight sharing to a sentiment analysis of movie reviews and to a biomedical search related to anemia. Their approach consistently yielded improved performance on classification tasks compared to strategies that did not exploit weight sharing. By improving core natural language processing technologies for automatic information extraction and classification of texts, Dr. Lease says web search engines built on these technologies can continue to improve.