Jeongwoo Ko

A probabilistic graphical model for joint answer ranking in question answering

Graphical models have been applied to various information retrieval and natural language processing tasks in the recent literature. In this paper, we apply a probabilistic graphical model for answer ranking in question answering. This model estimates the joint probability of correctness of all answer candidates, from which the probability of correctness of an individual candidate can be inferred. The joint prediction model can estimate both the correctness of individual answers as well as their correlations, which enables a list of accurate and comprehensive answers. This model was compared with a logistic regression model which directly estimates the probability of correctness of each individual answer candidate. An extensive set of empirical results based on TREC questions demonstrates the effectiveness of the joint model for answer ranking. Furthermore, we combine the joint model with the logistic regression model to improve the efficiency and accuracy of answer ranking.

Probabilistic models for answer-ranking in multilingual question-answering

This article presents two probabilistic models for answering ranking in the multilingual question-answering (QA) task, which finds exact answers to a natural language question written in different languages. Although some probabilistic methods have been utilized in traditional monolingual answer-ranking, limited prior research has been conducted for answer-ranking in multilingual question-answering with formal methods. This article first describes a probabilistic model that predicts the probabilities of correctness for individual answers in an independent way. It then proposes a novel probabilistic method to jointly predict the correctness of answers by considering both the correctness of individual answers as well as their correlations. As far as we know, this is the first probabilistic framework that proposes to model the correctness and correlation of answer candidates in multilingual question-answering and provide a novel approach to design a flexible and extensible system architecture for answer selection in multilingual QA. An extensive set of experiments were conducted to show the effectiveness of the proposed probabilistic methods in English-to-Chinese and English-to-Japanese cross-lingual QA, as well as English, Chinese, and Japanese monolingual QA using TREC and NTCIR questions.

Combining evidence with a probabilistic framework for answer ranking and answer merging in question answering

Question answering (QA) aims at finding exact answers to a users question from a large collection of documents. Most QA systems combine information retrieval with extraction techniques to identify a set of likely candidates and then utilize some ranking strategy to generate the final answers. This ranking process can be challenging, as it entails identifying the relevant answers amongst many irrelevant ones. This is more challenging in multi-strategy QA, in which multiple answering agents are used to extract answer candidates. As answer candidates come from different agents with different score distributions, how to merge answer candidates plays an important role in answer ranking. In this paper, we propose a unified probabilistic framework which combines multiple evidence to address challenges in answer ranking and answer merging. The hypotheses of the paper are that: (1) the framework effectively combines multiple evidence for identifying answer relevance and their correlation in answer ranking, (2) the framework supports answer merging on answer candidates returned by multiple extraction techniques, (3) the framework can support list questions as well as factoid questions, (4) the framework can be easily applied to a different QA system, and (5) the framework significantly improves performance of a QA system. An extensive set of experiments was done to support our hypotheses and demonstrate the effectiveness of the framework. All of the work substantially extends the preliminary research in Ko et al. (2007a). A probabilistic framework for answer selection in question answering. In: Proceedings of NAACL/HLT.

Analysis of an expert search query log

Expert search has made rapid progress in modeling, algorithms and evaluations in the recent years. However, there is very few work on analyzing how users interact with expert search systems. In this paper, we conduct analysis of an expert search query log. The aim is to understand the special characteristics of expert search usage. To the best of our knowledge, this is one of the earliest work on expert search query log analysis. We find that expert search users generally issue shorter queries, more common queries, and use more advanced search features, with fewer queries in a session, than general Web search users do. This study explores a new research direction in expert search by analyzing and exploiting query logs.

The KANTOO MT System: Controlled Language Checker and Lexical Maintenance Tool

We will present the KANTOO machine translation environment, a set of software servers and tools for multilingual document production. KANTOO includes modules for source language analysis, target language generation, source terminology management, target terminology management, and knowledge source development (see Figure 1).