1,788 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
Building a Test Collection for Significant-Event Detection in Arabic Tweets
With the increasing popularity of microblogging services like Twitter, researchers discov-
ered a rich medium for tackling real-life problems like event detection. However, event
detection in Twitter is often obstructed by the lack of public evaluation mechanisms
such as test collections (set of tweets, labels, and queries to measure the eectiveness of
an information retrieval system). The problem is more evident when non-English lan-
guages, e.g., Arabic, are concerned. With the recent surge of signicant events in the
Arab world, news agencies and decision makers rely on Twitters microblogging service to
obtain recent information on events. In this thesis, we address the problem of building a
test collection of Arabic tweets (named EveTAR) for the task of event detection.
To build EveTAR, we rst adopted an adequate denition of an event, which is a
signicant occurrence that takes place at a certain time. An occurrence is signicant if
there are news articles about it. We collected Arabic tweets using Twitter's streaming
API. Then, we identied a set of events from the Arabic data collection using Wikipedias
current events portal. Corresponding tweets were extracted by querying the Arabic data
collection with a set of manually-constructed queries. To obtain relevance judgments for
those tweets, we leveraged CrowdFlower's crowdsourcing platform.
Over a period of 4 weeks, we crawled over 590M tweets, from which we identied 66
events that cover 8 dierent categories and gathered more than 134k relevance judgments.
Each event contains an average of 779 relevant tweets. Over all events, we got an average
Kappa of 0.6, which is a substantially acceptable value. EveTAR was used to evalu-
ate three state-of-the-art event detection algorithms. The best performing algorithms
achieved 0.60 in F1 measure and 0.80 in both precision and recall. We plan to make
our test collection available for research, including events description, manually-crafted
queries to extract potentially-relevant tweets, and all judgments per tweet. EveTAR is
the rst Arabic test collection built from scratch for the task of event detection. Addi-
tionally, we show in our experiments that it supports other tasks like ad-hoc search
ON RELEVANCE FILTERING FOR REAL-TIME TWEET SUMMARIZATION
Real-time tweet summarization systems (RTS) require mechanisms for capturing relevant tweets, identifying novel tweets, and capturing timely tweets. In this thesis, we tackle the RTS problem with a main focus on the relevance filtering. We experimented with different traditional retrieval models.
Additionally, we propose two extensions to alleviate the sparsity and topic drift challenges that affect the relevance filtering. For the sparsity, we propose leveraging word embeddings in Vector Space model (VSM) term weighting to empower the system to use semantic similarity alongside the lexical matching. To mitigate the effect of topic drift, we exploit explicit relevance feedback to enhance profile representation to cope with its development in the stream over time.
We conducted extensive experiments over three standard English TREC test collections that were built specifically for RTS. Although the extensions do not generally exhibit better performance, they are comparable to the baselines used.
Moreover, we extended an event detection Arabic tweets test collection, called EveTAR, to support tasks that require novelty in the system's output. We collected novelty judgments using in-house annotators and used the collection to test our RTS system. We report preliminary results on EveTAR using different models of the RTS system.This work was made possible by NPRP grants # NPRP 7-1313-1-245 and # NPRP 7-1330-2-483 from the Qatar National Research Fund (a member of Qatar Foundation)
Sub-word indexing and blind relevance feedback for English, Bengali, Hindi, and Marathi IR
The Forum for Information Retrieval Evaluation (FIRE) provides document collections, topics, and relevance assessments for information retrieval (IR) experiments on Indian languages. Several research questions are explored in this paper: 1. how to create create a simple, languageindependent corpus-based stemmer, 2. how to identify sub-words and which types of sub-words are suitable as indexing units, and 3. how to apply blind relevance feedback on sub-words and how feedback term selection is affected by the type of the indexing unit. More than 140 IR experiments are conducted using the BM25 retrieval model on the topic titles and descriptions (TD) for the FIRE 2008 English, Bengali, Hindi, and Marathi document collections. The major findings are: The corpus-based stemming approach is effective as a knowledge-light
term conation step and useful in case of few language-specific resources. For English, the corpusbased
stemmer performs nearly as well as the Porter stemmer and significantly better than the baseline of indexing words when combined with query expansion. In combination with blind relevance feedback, it also performs significantly better than the baseline for Bengali and Marathi IR.
Sub-words such as consonant-vowel sequences and word prefixes can yield similar or better performance in comparison to word indexing. There is no best performing method for all languages. For English, indexing using the Porter stemmer performs best, for Bengali and Marathi, overlapping 3-grams obtain the best result, and for Hindi, 4-prefixes yield the highest MAP. However, in combination with blind relevance feedback using 10 documents and 20 terms, 6-prefixes for English and 4-prefixes for Bengali, Hindi, and Marathi IR yield the highest MAP. Sub-word identification is a general case of decompounding. It results in one or more index terms for a single word form and increases the number of index terms but decreases their average length. The corresponding retrieval experiments show that relevance feedback on sub-words benefits from
selecting a larger number of index terms in comparison with retrieval on word forms. Similarly, selecting the number of relevance feedback terms depending on the ratio of word vocabulary size to sub-word vocabulary size almost always slightly increases information retrieval effectiveness
compared to using a fixed number of terms for different languages
Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval
Although more and more language pairs are covered by machine translation
services, there are still many pairs that lack translation resources.
Cross-language information retrieval (CLIR) is an application which needs
translation functionality of a relatively low level of sophistication since
current models for information retrieval (IR) are still based on a
bag-of-words. The Web provides a vast resource for the automatic construction
of parallel corpora which can be used to train statistical translation models
automatically. The resulting translation models can be embedded in several ways
in a retrieval model. In this paper, we will investigate the problem of
automatically mining parallel texts from the Web and different ways of
integrating the translation models within the retrieval process. Our
experiments on standard test collections for CLIR show that the Web-based
translation models can surpass commercial MT systems in CLIR tasks. These
results open the perspective of constructing a fully automatic query
translation device for CLIR at a very low cost.Comment: 37 page
Evaluation campaigns and TRECVid
The TREC Video Retrieval Evaluation (TRECVid) is an
international benchmarking activity to encourage research
in video information retrieval by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVid completed its fifth annual cycle at the end of 2005 and in 2006 TRECVid will involve almost 70 research organizations, universities and other consortia. Throughout its existence, TRECVid has benchmarked both interactive and automatic/manual searching for shots from within a video
corpus, automatic detection of a variety of semantic and
low-level video features, shot boundary detection and the
detection of story boundaries in broadcast TV news. This
paper will give an introduction to information retrieval (IR) evaluation from both a user and a system perspective, highlighting that system evaluation is by far the most prevalent type of evaluation carried out. We also include a summary of TRECVid as an example of a system evaluation benchmarking campaign and this allows us to discuss whether
such campaigns are a good thing or a bad thing. There are
arguments for and against these campaigns and we present
some of them in the paper concluding that on balance they
have had a very positive impact on research progress
DCU@FIRE2010: term conflation, blind relevance feedback, and cross-language IR with manual and automatic query translation
For the first participation of Dublin City University (DCU)
in the FIRE 2010 evaluation campaign, information retrieval
(IR) experiments on English, Bengali, Hindi, and Marathi
documents were performed to investigate term conation
(different stemming approaches and indexing word prefixes),
blind relevance feedback, and manual and automatic query
translation. The experiments are based on BM25 and on
language modeling (LM) for IR. Results show that term conation always improves mean average precision (MAP)
compared to indexing unprocessed word forms, but different approaches seem to work best for different languages. For example, in monolingual Marathi experiments indexing 5-prefixes outperforms our corpus-based stemmer; in Hindi,
the corpus-based stemmer achieves a higher MAP. For Bengali, the LM retrieval model achieves a much higher MAP
than BM25 (0.4944 vs. 0.4526). In all experiments using
BM25, blind relevance feedback yields considerably higher
MAP in comparison to experiments without it. Bilingual IR experiments (English!Bengali and English!Hindi) are
based on query translations obtained from native speakers
and the Google translate web service. For the automatically
translated queries, MAP is slightly (but not significantly)
lower compared to experiments with manual query translations. The bilingual English!Bengali (English!Hindi)
experiments achieve 81.7%-83.3% (78.0%-80.6%) of the best
corresponding monolingual experiments
High-level feature detection from video in TRECVid: a 5-year retrospective of achievements
Successful and effective content-based access to digital
video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like
colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip.
The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work
done on the TRECVid high-level feature task, showing the
progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can
achieve large-scale, fast and reliable high-level feature detection on video
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