Patent search is the task of finding relevant existing patents, which is an important part of the patent's examiner's process of validating a patent application. In this paper, we studied how to transform a query patent (the application) into search queries. Three types of search features are explored for automatic query generation for patent search. Furthermore, different types of features are combined with a learning to rank method. Experiments based on a USPTO patent collection demonstrate that the single best search feature is the combination of words and noun-phrases from the summary field and the retrieval performance can be significantly improved by combining three types of search features.
Abstract. Retrieving semistructured (XML) data typically requires either a structured query such as XPath, or a keyword query that does not take structure into account. In this paper, we infer structural information automatically from keyword queries and incorporate this into a retrieval model. More specifically, we propose the concept of a mapping probability, which maps each query word into a related field (or XML element). This mapping probability is used as a weight to combine the language models estimated from each field. Experiments on two test collections show that our retrieval model based on mapping probabilities outperforms baseline techniques significantly.
Dealing with verbose (or long) queries poses a new challenge for information retrieval. Selecting a subset of the original query (a "sub-query") has been shown to be an effective method for improving these queries. In this paper, the distribution of sub-queries ("subset distribution") is formally modeled within a well-grounded framework. Specifically, sub-query selection is considered as a sequential labeling problem, where each query word in a verbose query is assigned a label of "keep" or "don't keep". A novel Conditional Random Field model is proposed to generate the distribution of sub-queries. This model captures the local and global dependencies between query words and directly optimizes the expected retrieval performance on a training set. The experiments, based on different retrieval models and performance measures, show that the proposed model can generate high-quality sub-query distributions and can significantly outperform state-of-the-art techniques.
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