491 research outputs found
Adapting Binary Information Retrieval Evaluation Metrics for Segment-based Retrieval Tasks
This report describes metrics for the evaluation of the effectiveness of
segment-based retrieval based on existing binary information retrieval metrics.
This metrics are described in the context of a task for the hyperlinking of
video segments. This evaluation approach re-uses existing evaluation measures
from the standard Cranfield evaluation paradigm. Our adaptation approach can in
principle be used with any kind of effectiveness measure that uses binary
relevance, and for other segment-baed retrieval tasks. In our video
hyperlinking setting, we use precision at a cut-off rank n and mean average
precision.Comment: Explanation of evaluation measures for the linking task of the
MediaEval Workshop 201
Chinese Character Decomposition for Neural MT with Multi-Word Expressions
Chinese character decomposition has been used as a feature to enhance Machine
Translation (MT) models, combining radicals into character and word level
models. Recent work has investigated ideograph or stroke level embedding.
However, questions remain about different decomposition levels of Chinese
character representations, radical and strokes, best suited for MT. To
investigate the impact of Chinese decomposition embedding in detail, i.e.,
radical, stroke, and intermediate levels, and how well these decompositions
represent the meaning of the original character sequences, we carry out
analysis with both automated and human evaluation of MT. Furthermore, we
investigate if the combination of decomposed Multiword Expressions (MWEs) can
enhance the model learning. MWE integration into MT has seen more than a decade
of exploration. However, decomposed MWEs has not previously been explored.Comment: Accepted to publish in NoDaLiDa202
How do Users Perceive Information: Analyzing user feedback while annotating textual units
ABSTRACT We describe an initial study of how participants perceive information when they categorize highlighted textual units within a document marked for a given information need. Our investigation explores how users look at different parts of the document and classify textual units within retrieved documents on 4-levels of relevance and importance. We compare how users classify different textual units within a document, and report mean and variance for different users across different topics. Further, we analyze and categorise the reasons provided by users while rating textual units within retrieved documents. This research shows some interesting observations regarding why some parts of the document are regarded as more relevant than others (e.g. it provides contextual information, contains background information) and which kind of information seems to be effective for satisfying the end users (e.g showing examples, providing facts) in a search task. This work is a part of our ongoing investigation into generation of effective surrogates and document summaries based on search topics and user interactions with information
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