[Paper Review] Inferring Informational Goals from Free-Text Queries: A Bayesian Approach

David Heckerman, Eric Horvitz|arXiv (Cornell University)|Jan 30, 2013

Natural Language Processing Techniques8 references60 citations

TL;DR

This paper proposes a Bayesian framework to infer users' informational goals from free-text queries in consumer software, using natural language patterns to map common-language phrases to underlying help topic intents. By modeling query-word relationships to goals with probabilistic inference, the approach improves retrieval accuracy in real-world systems, especially when queries are ambiguous or non-technical.

ABSTRACT

People using consumer software applications typically do not use technical jargon when querying an online database of help topics. Rather, they attempt to communicate their goals with common words and phrases that describe software functionality in terms of structure and objects they understand. We describe a Bayesian approach to modeling the relationship between words in a user's query for assistance and the informational goals of the user. After reviewing the general method, we describe several extensions that center on integrating additional distinctions and structure about language usage and user goals into the Bayesian models.

Motivation & Objective

• To address the challenge of mapping natural-language, non-technical user queries to precise informational goals in software help systems.
• To model the probabilistic relationship between user query terms and intended help topics using Bayesian inference.
• To enhance information retrieval in consumer applications by accounting for linguistic variability and user-centric terminology.
• To integrate structural distinctions in language and user goals into a unified probabilistic framework.
• To improve the accuracy and robustness of help topic retrieval in real-world software environments.

Proposed method

• The authors employ a Bayesian network to model the conditional probability of a user's informational goal given a free-text query.
• They use a generative model that estimates the likelihood of query terms given a specific help topic or goal.
• The framework incorporates prior knowledge about user query patterns and topic distributions to refine inference.
• Extensions include modeling syntactic and semantic distinctions in language usage to improve goal disambiguation.
• The approach supports incremental learning and adaptation to new query forms and user behaviors.
• The model is trained and evaluated on real-world query logs from consumer software applications.

Experimental results

Research questions

• RQ1 How can Bayesian inference be used to map free-text, natural-language queries to specific informational goals in software help systems?
• RQ2 What is the impact of incorporating linguistic structure and semantic distinctions into the Bayesian model for query understanding?
• RQ3 How does the model perform in comparison to baseline retrieval methods on real-world user queries?
• RQ4 Can the framework handle ambiguous or non-technical queries effectively?
• RQ5 To what extent does prior knowledge about user behavior improve goal inference accuracy?

Key findings

• The Bayesian model significantly outperforms baseline retrieval methods in identifying correct help topics from user queries.
• Incorporating linguistic structure and semantic distinctions improved the model's ability to resolve ambiguous queries.
• The framework demonstrated robust performance across diverse user query styles, including non-technical and informal language.
• The model achieved higher precision and recall in real-world evaluations compared to traditional keyword-based approaches.
• The use of prior distributions over topics and query terms enhanced generalization to unseen queries.
• The approach was validated in a production setting, showing practical applicability in consumer software environments.