Topic
Ranking (information retrieval)
About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.
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02 Feb 2018TL;DR: HyperQA as mentioned in this paper proposes a pairwise ranking objective that models the relationship between question and answer embeddings in Hyperbolic space instead of Euclidean space, which enables automatic discovery of latent hierarchies.
Abstract: The dominant neural architectures in question answer retrieval are based on recurrent or convolutional encoders configured with complex word matching layers. Given that recent architectural innovations are mostly new word interaction layers or attention-based matching mechanisms, it seems to be a well-established fact that these components are mandatory for good performance. Unfortunately, the memory and computation cost incurred by these complex mechanisms are undesirable for practical applications. As such, this paper tackles the question of whether it is possible to achieve competitive performance with simple neural architectures. We propose a simple but novel deep learning architecture for fast and efficient question-answer ranking and retrieval. More specifically, our proposed model, HyperQA, is a parameter efficient neural network that outperforms other parameter intensive models such as Attentive Pooling BiLSTMs and Multi-Perspective CNNs on multiple QA benchmarks. The novelty behind HyperQA is a pairwise ranking objective that models the relationship between question and answer embeddings in Hyperbolic space instead of Euclidean space. This empowers our model with a self-organizing ability and enables automatic discovery of latent hierarchies while learning embeddings of questions and answers. Our model requires no feature engineering, no similarity matrix matching, no complicated attention mechanisms nor over-parameterized layers and yet outperforms and remains competitive to many models that have these functionalities on multiple benchmarks.
102 citations
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01 Sep 2001TL;DR: A user study and software system designed to provide users with lightweight feedback about opaque query transformations suggest that users do indeed have difficulties understanding the operation of query transformations without additional assistance.
Abstract: Typically, commercial Web search engines provide very little feedback to the user concerning how a particular query is processed and interpreted. Specifically, they apply key query transformations without the users knowledge. Although these transformations have a pronounced effect on query results, users have very few resources for recognizing their existence and understanding their practical importance. We conducted a user study to gain a better understanding of users knowledge of and reactions to the operation of several query transformations that web search engines automatically employ. Additionally, we developed and evaluated Transparent Queries, a software system designed to provide users with lightweight feedback about opaque query transformations. The results of the study suggest that users do indeed have difficulties understanding the operation of query transformations without additional assistance. Finally, although transparency is helpful and valuable, interfaces that allow direct control of query transformations might ultimately be more helpful for end-users.
102 citations
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TL;DR: This work introduces an image retrieval model based on Bayesian belief networks that indicates that retrieval using an image surrounding text passages is as effective as standard retrieval based on HTML tags.
Abstract: The World Wide Web is the largest publicly available image repository and a natural source of attention. An immediate consequence is that searching for images on the Web has become a current and important task. To search for images of interest, the most direct approach is keyword-based searching. However, since images on the Web are poorly labeled, direct application of standard keyword-based image searching techniques frequently yields poor results. We propose a comprehensive solution to this problem. In our approach, multiple sources of evidence related to the images are considered. To allow combining these distinct sources of evidence, we introduce an image retrieval model based on Bayesian belief networks. To evaluate our approach, we perform experiments on a reference collection composed of 54000 Web images. Our results indicate that retrieval using an image surrounding text passages is as effective as standard retrieval based on HTML tags. This is an interesting result because current image search engines in the Web usually do not take text passages into consideration. Most important, according to our results, the combination of information derived from text passages with information derived from HTML tags leads to improved retrieval, with relative gains in average precision figures of roughly 50 percent, when compared to the results obtained by the use of each source of evidence in isolation.
101 citations
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TL;DR: XIRQL ("circle") is an XML query language that incorporates imprecision and vagueness for both structural and content-oriented query conditions, and is processed by the HyREX retrieval engine.
Abstract: XIRQL ("circle") is an XML query language that incorporates imprecision and vagueness for both structural and content-oriented query conditions. The corresponding uncertainty is handled by a consistent probabilistic model. The core features of XIRQL are (1) document ranking based on index term weighting, (2) specificity-oriented search for retrieving the most relevant parts of documents, (3) datatypes with vague predicates for dealing with specific types of content and (4) structural vagueness for vague interpretation of structural query conditions. A XIRQL database may contain several classes of documents, where all documents in a class conform to the same DTD; links between documents also are supported. XIRQL queries are translated into a path algebra, which can be processed by our HyREX retrieval engine.
101 citations
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15 Oct 2019
TL;DR: With W2VV++, a super version of Word2VisualVec previously developed for visual-to-text matching, a new baseline for ad-hoc video search is established, which outperforms the state-of-the-art.
Abstract: Ad-hoc video search (AVS) is an important yet challenging problem in multimedia retrieval. Different from previous concept-based methods, we propose a fully deep learning method for query representation learning. The proposed method requires no explicit concept modeling, matching and selection. The backbone of our method is the proposed W2VV++ model, a super version of Word2VisualVec (W2VV) previously developed for visual-to-text matching. W2VV++ is obtained by tweaking W2VV with a better sentence encoding strategy and an improved triplet ranking loss. With these simple yet important changes, W2VV++ brings in a substantial improvement. As our participation in the TRECVID 2018 AVS task and retrospective experiments on the TRECVID 2016 and 2017 data show, our best single model, with an overall inferred average precision (infAP) of 0.157, outperforms the state-of-the-art. The performance can be further boosted by model ensemble using late average fusion, reaching a higher infAP of 0.163. With W2VV++, we establish a new baseline for ad-hoc video search.
101 citations