According to common relevance-judgments regimes, such as TREC's, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: document ranking methods that induce information from the document's passages. However, the main source of passage-based information utilized was passage-query similarities. We address the challenge of utilizing richer sources of passage-based information to improve document retrieval effectiveness. Specifically, we devise a suite of learning-to-rank-based document retrieval methods that utilize an effective ranking of passages produced in response to the query; the passage ranking is also induced using a learning-to-rank approach. Some of the methods quantify the ranking of the passages of a document. Others utilize the feature-based representation of passages used for learning a passage ranker. Empirical evaluation attests to the clear merits of our methods with respect to highly effective baselines. Our best performing method is based on learning a document ranking function using document-query features and passage-query features of the document's passage most highly ranked.
Recently, @cite_15 presented a method that selects (or generates) a passage from a document in response to a query using information induced from a community question answering system. Then, features of the passage (not necessarily those used for selecting the passage) along with document features are used to represent the document. This approach is reminiscent of our method which uses passage features and document features to represent a document. There are, however, major differences between the two. Our method is not based on an external resource. Furthermore, we utilize passage ranking that is induced using a learning-to-rank approach with passage features while in @cite_15 this is not the case. In addition, the passage features used in our method are the same as those used for ranking passages which is not the case in @cite_15 . We demonstrate the merits of using the passage features that are used for (effective) passage ranking to represent a document. We also show the merits of using passage-relevance prior estimates adopted from work on Web retrieval to rank passages. Some of these estimates were used by Yulianti+ @cite_15 to rank documents but not passages.
@cite_15
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Evidence derived from passages that closely represent likely answers to a posed query can be useful input to the ranking process. Based on a novel use of Community Question Answering data, we present an approach for the creation of such passages. A general framework for extracting answer passages and estimating their quality is proposed, and this evidence is integrated into ranking models. Our experiments on two web collections show that such quality estimates from answer passages provide a strong indication of document relevance and compare favorably to previous passage-based methods. Combining such evidence can significantly improve over a set of state-of-the-art ranking models, including Quality-Biased Ranking, External Expansion, and a combination of both. A final ranking model that incorporates all quality estimates achieves further improvements on both collections. UIUC participated in the HARD track in TREC 2004 and focused on the evaluation of a new method for identifying variable-length passages using HMMs. Most existing approaches to passage retrieval rely on pre-segmentation of documents, but the optimal boundaries of a relevant passage depends on both the query and the document. Our new method aims at determining or improving the boundaries of a relevant passage based on both the query and topical coherence in the document. In this paper, we describe the method and present analysis of our HARD 2004 evaluation results. The results show that the HMM method can improve the boundaries of pre-segmented passages in terms of overall passage retrieval accuracy and recall, but at the price of precision sometimes. However, due to the non-optimality of the relevance feedback procedure and the poor ranking performance based on passage scoring, the best of our passage runs is still worse than a whole document baseline run. Further experiments and analysis are needed to fully understand why the language modeling approach did not work well on passage scoring. Retrieving topically-relevant text passages in documents has been studied many times, but finding non-factoid, multiple sentence answers to web queries is a different task that is becoming increasingly important for applications such as mobile search. As the first stage of developing retrieval models for "answer passages", we describe the process of creating a test collection of questions and multiple-sentence answers based on the TREC GOV2 queries and documents. This annotation shows that most of the description-length TREC queries do in fact have passage-level answers. We then examine the effectiveness of current passage retrieval models in terms of finding passages that contain answers. We show that the existing methods are not effective for this task, and also observe that the relative performance of these methods in retrieving answers does not correspond to their performance in retrieving relevant documents. This dissertation investigates the role of contextual information in the automated retrieval and display of full-text documents, using robust natural language processing algorithms to automatically detect structure in and assign topic labels to texts. Many long texts are comprised of complex topic and subtopic structure, a fact ignored by existing information access methods. I present two algorithms which detect such structure, and two visual display paradigms which use the results of these algorithms to show the interactions of multiple main topics, multiple subtopics, and the relations between main topics and subtopics. The first algorithm, called TextTiling , recognizes the subtopic structure of texts as dictated by their content. It uses domain-independent lexical frequency and distribution information to partition texts into multi-paragraph passages. The results are found to correspond well to reader judgments of major subtopic boundaries. The second algorithm assigns multiple main topic labels to each text, where the labels are chosen from pre-defined, intuitive category sets; the algorithm is trained on unlabeled text. A new iconic representation, called TileBars uses TextTiles to simultaneously and compactly display query term frequency, query term distribution and relative document length. This representation provides an informative alternative to ranking long texts according to their overall similarity to a query. For example, a user can choose to view those documents that have an extended discussion of one set of terms and a brief but overlapping discussion of a second set of terms. This representation also allows for relevance feedback on patterns of term distribution. TileBars display documents only in terms of words supplied in the user query. For a given retrieved text, if the query words do not correspond to its main topics, the user cannot discern in what context the query terms were used. For example, a query on contaminants may retrieve documents whose main topics relate to nuclear power, food, or oil spills. To address this issue, I describe a graphical interface, called Cougar , that displays retrieved documents in terms of interactions among their automatically-assigned main topics, thus allowing users to familiarize themselves with the topics and terminology of a text collection.