93,803 research outputs found
Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007
This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p
Bibliometric cartography of information retrieval research by using co-word analysis
The aim of this study is to map the intellectual structure of the field of Information Retrieval (IR) during the period of 1987-1997. Co-word analysis was employed to reveal patterns and trends in the IR field by measuring the association strengths of terms representative of relevant publications or other texts produced in IR field. Data were collected from Science Citation Index (SCI) and Social Science Citation Index (SSCI) for the period of 1987-1997. In addition to the keywords added by the SCI and SSCI databases, other important keywords were extracted from titles and abstracts manually. These keywords were further standardized using vocabulary control tools. In order to trace the dynamic changes of the IR field, the whole 11-year period was further separated into two consecutive periods: 1987-1991 and 1992-1997. The results show that the IR field has some established research themes and it also changes rapidly to embrace new themes
An Attribute Selection For Severity Level Determination According To The Support Vector Machine Classification Result
Determination of bug severity level is needed in fixing bug. Actually, in bug-tracking system, there is around 14 attributes used for defining a bug. But, all this time we do not know which attributes are highly influential for this.
In this research, a new model of severity type classification using Infogain method for Bugzilla is proposed. As for the classsification process, we use Support Vector Machine, because this method is suitable in handling a massive data records. In this research, 8 bug attributes and 17.746 record of bug reports are involved.
From the result of the experiment, we recommend five attributes which can be used effectively in classifying the severity types with a minimal value of infogain 0,33 which is component, qa_contact, summary, cc_list and product. The combination of those 5 attributes resulting in 99,83% accuracy of severity types classification.
Keywords- Bug Tracking System; Severity Level Classification; TF-IDF; Infogain; SVM
Words are Malleable: Computing Semantic Shifts in Political and Media Discourse
Recently, researchers started to pay attention to the detection of temporal
shifts in the meaning of words. However, most (if not all) of these approaches
restricted their efforts to uncovering change over time, thus neglecting other
valuable dimensions such as social or political variability. We propose an
approach for detecting semantic shifts between different viewpoints--broadly
defined as a set of texts that share a specific metadata feature, which can be
a time-period, but also a social entity such as a political party. For each
viewpoint, we learn a semantic space in which each word is represented as a low
dimensional neural embedded vector. The challenge is to compare the meaning of
a word in one space to its meaning in another space and measure the size of the
semantic shifts. We compare the effectiveness of a measure based on optimal
transformations between the two spaces with a measure based on the similarity
of the neighbors of the word in the respective spaces. Our experiments
demonstrate that the combination of these two performs best. We show that the
semantic shifts not only occur over time, but also along different viewpoints
in a short period of time. For evaluation, we demonstrate how this approach
captures meaningful semantic shifts and can help improve other tasks such as
the contrastive viewpoint summarization and ideology detection (measured as
classification accuracy) in political texts. We also show that the two laws of
semantic change which were empirically shown to hold for temporal shifts also
hold for shifts across viewpoints. These laws state that frequent words are
less likely to shift meaning while words with many senses are more likely to do
so.Comment: In Proceedings of the 26th ACM International on Conference on
Information and Knowledge Management (CIKM2017
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