Eric W. Brown

Question-answering by predictive annotation

We present a new technique for question answering called Predictive Annotation. Predictive Annotation identifies potential answers to questions in text, annotates them accordingly and indexes them. This technique, along with a complementary analysis of questions, passage-level ranking and answer selection, produces a system effective at answering natural-language fact-seeking questions posed against large document collections. Experimental results show the effects of different parameter settings and lead to a number of general observations about the question-answering problem.

Detecting similar documents using salient terms

We describe a system for rapidly determining document similarity among a set of documents obtained from an information retrieval (IR) system. We obtain a ranked list of the most important terms in each document using a rapid phrase recognizer system. We store these in a database and compute document similarity using a simple database query. If the number of terms found to not be contained in both documents is less than some predetermined threshold compared to the total number of terms in the document, these documents are determined to be very similar. We compare this to the shingles approach.

Making Watson fast

IBM Watson™ is a system created to demonstrate DeepQA technology by competing against human champions in a question-answering game designed for people. The DeepQA architecture was designed to be massively parallel, with an expectation that low latency response times could be achieved by doing parallel computation on many computers. This paper describes how a large set of deep natural-language processing programs were integrated into a single application, scaled out across thousands of central processing unit cores, and optimized to run fast enough to compete in live Jeopardy!™ games.

Toward speech as a knowledge resource

Speech is a tantalizing mode of human communication. On the one hand, humans understand speech with ease and use speech to express complex ideas, information, and knowledge. On the other hand, automatic speech recognition with computers is very hard, and extracting knowledge from speech is even harder. Nevertheless, the potential reward for solving this problem drives us to pursue it. Before we can exploit speech as a knowledge resource, however, we must understand the current state of the art in speech recognition and the relevant, successful applications of speech recognition in the related areas of multimedia indexing and search. In this paper we advocate the study of speech as a knowledge resource, provide a brief introduction to the state of the art in speech recognition, describe a number of systems that use speech recognition to enable multimedia analysis, indexing, and search, and present a number of exploratory applications of speech recognition that move toward the goal of exploiting speech as a knowledge resource.

Speech transcript analysis for automatic search

We address the problem of finding collateral information pertinent to a live television broadcast in real time. The solution starts with a text transcript of the broadcast generated by an automatic speech recognition system. Speaker independent speech recognition technology, even when tailored for a broadcast scenario, generally produces transcripts with relatively low accuracy. Given this limitation, we have developed algorithms that can determine the essence of the broadcast from these transcripts. Specifically, we extract named entities, topics, and sentence types from the transcript and use them to automatically generate both structured and unstructured search queries. A novel distance-ranking algorithm is used to select relevant information from the search results. The whole process is performed online and the query results (i.e., the collateral information) are added to the broadcast stream.

Information retrieval techniques for speech applications

Traditional Information Retrieval Techniques.- Perspectives on Information Retrieval and Speech.- Spoken Document Pre-processing.- Capitalization Recovery for Text.- Adapting IR Techniques to Spoken Documents.- Clustering of Imperfect Transcripts Using a Novel Similarity Measure.- Extracting Keyphrases from Spoken Audio Documents.- Segmenting Conversations by Topic, Initiative, and Style.- Extracting Caller Information from Voicemail.- Techniques for Multi-media Collections.- Speech and Hand Transcribed Retrieval.- New Applications.- The Use of Speech Retrieval Systems: A Study Design.- Speech-Driven Text Retrieval: Using Target IR Collections for Statistical Language Model Adaptation in Speech Recognition.- WASABI: Framework for Real-Time Speech Analysis Applications (Demo).

Towards speech as a knowledge resource

Speech is a tantalizing mode of human communication. On the one hand, humans understand speech with ease and use speech to express complex ideas, information, and knowledge. On the other hand, automatic speech recognition with computers is still very hard, and extracting knowledge from speech is even harder. In this paper we motivate the study of speech as a knowledge resource and briefly survey a family of related applications and systems being developed at IBM Research aimed towards the goal of exploiting speech as a knowledge resource.

A novel method for detecting similar documents

We describe a system for rapidly determining document similarity among a set of documents obtained from an information retrieval (IR) system. We obtain a ranked list of the most important terms in each document using a rapid phrase recognizer system. We store these in a database and compute document similarity using a simple database query. If the number of terms found to not be contained in both documents is less than some predetermined threshold compared to the total number of terms in the document, these documents are determined to be very similar.

Special questions and techniques

Jeopardy!™ questions represent a wide variety of question types. The vast majority are Standard Jeopardy! Questions, where the question contains one or more assertions about some unnamed entity or concept, and the task is to identify the described entity or concept. This style of question is a representative of a wide range of common question-answering tasks, and the bulk of the IBM Watsoni system is focused on solving this problem. A small percentage of Jeopardy! questions require a specialized procedure to derive an answer or some derived assertion about the answer. We call any question that requires such a specialized computational procedure, selected on the basis of a unique classification of the question, a Special Jeopardy! Question. Although Special Questions per se are typically less relevant in broader question-answering applications, they are an important class of question to address in the Jeopardy! context. Moreover, the design of our Special Question solving procedures motivated architectural design decisions that are applicable to general open-domain question-answering systems. We explore these rarer classes of questions here and describe and evaluate the techniques that we developed to solve these questions.

Artificial Intelligence in Medical Practice: the Question to the Answer?

Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AIs future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence. AI is being successfully applied for image analysis in radiology, pathology, and dermatology, with diagnostic speed exceeding, and accuracy paralleling, medical experts. While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records. In this and other ways, AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials.