2,464 research outputs found
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets
This article introduces a new language-independent approach for creating a
large-scale high-quality test collection of tweets that supports multiple
information retrieval (IR) tasks without running a shared-task campaign. The
adopted approach (demonstrated over Arabic tweets) designs the collection
around significant (i.e., popular) events, which enables the development of
topics that represent frequent information needs of Twitter users for which
rich content exists. That inherently facilitates the support of multiple tasks
that generally revolve around events, namely event detection, ad-hoc search,
timeline generation, and real-time summarization. The key highlights of the
approach include diversifying the judgment pool via interactive search and
multiple manually-crafted queries per topic, collecting high-quality
annotations via crowd-workers for relevancy and in-house annotators for
novelty, filtering out low-agreement topics and inaccessible tweets, and
providing multiple subsets of the collection for better availability. Applying
our methodology on Arabic tweets resulted in EveTAR , the first
freely-available tweet test collection for multiple IR tasks. EveTAR includes a
crawl of 355M Arabic tweets and covers 50 significant events for which about
62K tweets were judged with substantial average inter-annotator agreement
(Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating
existing algorithms in the respective tasks. Results indicate that the new
collection can support reliable ranking of IR systems that is comparable to
similar TREC collections, while providing strong baseline results for future
studies over Arabic tweets
Retrieving descriptive phrases from large amounts of free text
This paper presents a system that retrieves descriptive phrases of proper nouns from free text. Sentences holding the specified noun are ranked using a technique based on pattern matching, word counting, and sentence location. No domain specific knowledge is used. Experiments show the system able to rank highly those sentences that contain phrases describing or defining the query noun. In contrast to existing methods, this system does not use parsing techniques but still achieves high levels of accuracy. From the results of a large-scale experiment, it is speculated that the success of this simpler method is due to the high quantities of free text being searched. Parallels between this work and recent findings in the very large corpus track of TREC are drawn
A Comparison of Nuggets and Clusters for Evaluating Timeline Summaries
There is growing interest in systems that generate timeline summaries by filtering high-volume streams of documents to retain only those that are relevant to a particular event or topic. Continued advances in algorithms and techniques for this task depend on standardized and reproducible evaluation methodologies for comparing systems. However, timeline summary evaluation is still in its infancy, with competing methodologies currently being explored in international evaluation forums such as TREC. One area of active exploration is how to explicitly represent the units of information that should appear in a 'good' summary. Currently, there are two main approaches, one based on identifying nuggets in an external 'ground truth', and the other based on clustering system outputs. In this paper, by building test collections that have both nugget and cluster annotations, we are able to compare these two approaches. Specifically, we address questions related to evaluation effort, differences in the final evaluation products, and correlations between scores and rankings generated by both approaches. We summarize advantages and disadvantages of nuggets and clusters to offer recommendations for future system evaluation
An analysis of machine translation errors on the effectiveness of an Arabic-English QA system
The aim of this paper is to investigate
how much the effectiveness of a Question
Answering (QA) system was affected
by the performance of Machine
Translation (MT) based question translation.
Nearly 200 questions were selected
from TREC QA tracks and ran through a
question answering system. It was able to
answer 42.6% of the questions correctly
in a monolingual run. These questions
were then translated manually from English
into Arabic and back into English using
an MT system, and then re-applied to
the QA system. The system was able to
answer 10.2% of the translated questions.
An analysis of what sort of translation error
affected which questions was conducted,
concluding that factoid type
questions are less prone to translation error
than others
Entity Ranking on Graphs: Studies on Expert Finding
Todays web search engines try to offer services for finding various information in addition to simple web pages, like showing locations or answering simple fact queries. Understanding the association of named entities and documents is one of the key steps towards such semantic search tasks. This paper addresses the ranking of entities and models it in a graph-based relevance propagation framework. In particular we study the problem of expert finding as an example of an entity ranking task. Entity containment graphs are introduced that represent the relationship between text fragments on the one hand and their contained entities on the other hand. The paper shows how these graphs can be used to propagate relevance information from the pre-ranked text fragments to their entities. We use this propagation framework to model existing approaches to expert finding based on the entity's indegree and extend them by recursive relevance propagation based on a probabilistic random walk over the entity containment graphs. Experiments on the TREC expert search task compare the retrieval performance of the different graph and propagation models
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