3,400 research outputs found
Measuring the differences between human-human and human-machine dialogs
In this paper, we assess the applicability of user simulation techniques to generate dialogs which are similar to real human-machine spoken interactions.To do so, we present the results of the comparison between three corpora acquired by means of different techniques. The first corpus was acquired with real users.A statistical user simulation technique has been applied to the same task to acquire the second corpus. In this technique, the next user answer is selected by means of a classification process that takes into account the previous dialog history, the lexical information in the clause, and the subtask of the dialog to which it contributes. Finally, a dialog simulation technique has been developed for the acquisition of the third corpus. This technique uses a random selection of the user and system turns, defining stop conditions for automatically deciding if the simulated dialog is successful or not. We use several evaluation measures proposed in previous research to compare between our three acquired corpora, and then discuss the similarities and differences with regard to these measures
Performance of IR Models on Duplicate Bug Report Detection: A Comparative Study
Open source projects incorporate bug triagers to help with the task of bug report
assignment to developers. One of the tasks of a triager is to identify whether an incoming
bug report is a duplicate of a pre-existing report. In order to detect duplicate bug reports,
a triager either relies on his memory and experience or on the search capabilties of the bug
repository. Both these approaches can be time consuming for the triager and may also
lead to the misidentication of duplicates. It has also been suggested that duplicate bug
reports are not necessarily harmful, instead they can complement each other to provide
additional information for developers to investigate the defect at hand. This motivates the
need for automated or semi-automated techniques for duplicate bug detection.
In the literature, two main approaches have been proposed to solve this problem. The
first approach is to prevent duplicate reports from reaching developers by automatically
filtering them while the second approach deals with providing the triager a list of top-N
similar bug reports, allowing the triager to compare the incoming bug report with the ones
provided in the list. Previous works have tried to enhance the quality of the suggested
lists, but the approaches either suffered a poor Recall Rate or they incurred additional
runtime overhead, making the deployment of a retrieval system impractical. To the extent
of our knowledge, there has been little work done to do an exhaustive comparison of
the performance of different Information Retrieval Models (especially using more recent
techniques such as topic modeling) on this problem and understanding the effectiveness of
different heuristics across various application domains.
In this thesis, we compare the performance of word based models (derivatives of the
Vector Space Model) such as TF-IDF, Log-Entropy with that of topic based models such as
Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA) and Random Indexing
(RI). We leverage heuristics that incorporate exception stack frames, surface features,
summary and long description from the free-form text in the bug report. We perform
experiments on subsets of bug reports from Eclipse and Firefox and achieve a recall rate of
60% and 58% respectively. We find that word based models, in particular a Log-Entropy
based weighting scheme, outperform topic based ones such as LSI and LDA.
Using historical bug data from Eclipse and NetBeans, we determine the optimal time
frame for a desired level of duplicate bug report coverage. We realize an Online Duplicate
Detection Framework that uses a sliding window of a constant time frame as a first step
towards simulating incoming bug reports and recommending duplicates to the end user
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
Living analytics methods for the social web
[no abstract
Computational approaches to semantic change (Volume 6)
Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
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