15,169 research outputs found
A survey on the use of relevance feedback for information access systems
Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems
A study of query expansion methods for patent retrieval
Patent retrieval is a recall-oriented search task where the objective is to find all possible relevant documents. Queries in patent retrieval are typically very long since they take the form of a patent claim or even a full patent application in the case of priorart patent search. Nevertheless, there is generally a significant mismatch between the query and the relevant documents, often leading to low retrieval effectiveness. Some previous work has
tried to address this mismatch through the application of query expansion (QE) techniques which have generally showed
effectiveness for many other retrieval tasks. However, results of QE on patent search have been found to be very disappointing. We present a review of previous investigations of QE in patent retrieval, and explore some of these techniques on a prior-art patent search task. In addition, a novel method for QE using automatically generated synonyms set is presented. While previous QE techniques fail to improve over baseline retrieval, our new approach show statistically better retrieval precision over
the baseline, although not for recall. In addition, it proves to be significantly more efficient than existing techniques. An extensive analysis to the results is presented which seeks to better understand situations where these QE techniques succeed or fail
Reply With: Proactive Recommendation of Email Attachments
Email responses often contain items-such as a file or a hyperlink to an
external document-that are attached to or included inline in the body of the
message. Analysis of an enterprise email corpus reveals that 35% of the time
when users include these items as part of their response, the attachable item
is already present in their inbox or sent folder. A modern email client can
proactively retrieve relevant attachable items from the user's past emails
based on the context of the current conversation, and recommend them for
inclusion, to reduce the time and effort involved in composing the response. In
this paper, we propose a weakly supervised learning framework for recommending
attachable items to the user. As email search systems are commonly available,
we constrain the recommendation task to formulating effective search queries
from the context of the conversations. The query is submitted to an existing IR
system to retrieve relevant items for attachment. We also present a novel
strategy for generating labels from an email corpus---without the need for
manual annotations---that can be used to train and evaluate the query
formulation model. In addition, we describe a deep convolutional neural network
that demonstrates satisfactory performance on this query formulation task when
evaluated on the publicly available Avocado dataset and a proprietary dataset
of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on
Information and Knowledge Management. 201
Learning a Deep Listwise Context Model for Ranking Refinement
Learning to rank has been intensively studied and widely applied in
information retrieval. Typically, a global ranking function is learned from a
set of labeled data, which can achieve good performance on average but may be
suboptimal for individual queries by ignoring the fact that relevant documents
for different queries may have different distributions in the feature space.
Inspired by the idea of pseudo relevance feedback where top ranked documents,
which we refer as the \textit{local ranking context}, can provide important
information about the query's characteristics, we propose to use the inherent
feature distributions of the top results to learn a Deep Listwise Context Model
that helps us fine tune the initial ranked list. Specifically, we employ a
recurrent neural network to sequentially encode the top results using their
feature vectors, learn a local context model and use it to re-rank the top
results. There are three merits with our model: (1) Our model can capture the
local ranking context based on the complex interactions between top results
using a deep neural network; (2) Our model can be built upon existing
learning-to-rank methods by directly using their extracted feature vectors; (3)
Our model is trained with an attention-based loss function, which is more
effective and efficient than many existing listwise methods. Experimental
results show that the proposed model can significantly improve the
state-of-the-art learning to rank methods on benchmark retrieval corpora
- âŠ