Jeffrey Dalton

Entity query feature expansion using knowledge base links

Recent advances in automatic entity linking and knowledge base construction have resulted in entity annotations for document and query collections. For example, annotations of entities from large general purpose knowledge bases, such as Freebase and the Google Knowledge Graph. Understanding how to leverage these entity annotations of text to improve ad hoc document retrieval is an open research area. Query expansion is a commonly used technique to improve retrieval effectiveness. Most previous query expansion approaches focus on text, mainly using unigram concepts. In this paper, we propose a new technique, called entity query feature expansion (EQFE) which enriches the query with features from entities and their links to knowledge bases, including structured attributes and text. We experiment using both explicit query entity annotations and latent entities. We evaluate our technique on TREC text collections automatically annotated with knowledge base entity links, including the Google Freebase Annotations (FACC1) data. We find that entity-based feature expansion results in significant improvements in retrieval effectiveness over state-of-the-art text expansion approaches.

Coreference aware web object retrieval

As user demands become increasingly sophisticated, search engines today are competing in more than just returning document results from the Web. One area of competition is providing web object results from structured data extracted from a multitude of information sources. We address the problem of performing keyword retrieval over a collection of objects containing a large degree of duplication as different Web-based information sources provide descriptions of the same object. We develop a method for coreference aware retrieval that performs topic-specific coreference resolution on retrieved objects in order to improve object search results. Our results demonstrate that coreference has a significant impact on the effectiveness of retrieval in the domain of local search. Our results show that a coreference aware system outperforms naive object retrieval by more than 20% in P5 and P10.

Passage retrieval for incorporating global evidence in sequence labeling

Many forms of linguistic analysis, such as part of speech tagging, named entity recognition, and other sequence labeling tasks are performed on short spans of text and assume statistical dependence within a window of only a few tokens. We propose using passage retrieval to induce non-local dependencies in structured classification that generalizes earlier work in context aggregation for named-entity recognition. We introduce a new method for feature expansion inspired by psuedo-relevance feedback (PRF). Our results on the CoNLL 2003 task show that features from cross-document feature expansion improves NER effectiveness over previous aggregation models. Utilizing all the tokens in a sentence for query context consistently perform best on both intrinsic and extrinsic evaluations. Tagging models incorporating feature expansion outperform the leading NER system when evaluated on out of domain data, a collection of publicly available scanned books on the topic of historic Deerfield, MA. Finally, the results show that retrieval based feature expansion using an external collection of unlabeled text can result in further effectiveness improvements.

Characterizing the subjectivity of topics

A document or web page in isolation may appear completely reasonable, but may represent a biased perspective on the topic being discussed. Given the topic of a document, we propose new metrics provocativeness and balance that suggest when the topic could be controversial. We explore the use of these metrics to characterize the subjectivity of the topics in the TREC Blog Track.

Search and exploration of scanned books

In this demo, we present Proteus, a novel interface for interacting with multiple retrieval types extracted from scanned books provided by the Internet Archive. The primary purpose of Proteus is to provide a rich interactive experience for users to explore collections with automatically extracted and linked entity data. The system supports seamlessly shifting perspectives between books, entities, and topics. Proteus provides a starting point for a variety of exploratory search tasks.

A framework for manipulating and searching multiple retrieval types

Conventional retrieval systems view documents as a unit and look at different retrieval types within a document. We introduce Proteus, a frame-work for seamlessly navigating books as dynamic collections which are defined on the fly. Proteus allows us to search various retrieval types. Navigable types include pages, books, named persons, locations, and pictures in a collection of books taken from the Internet Archive. The demonstration shows the value of multi-type browsing in dynamic collections to peruse new data.