886 research outputs found
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Matching natural language sentences is central for many applications such as
information retrieval and question answering. Existing deep models rely on a
single sentence representation or multiple granularity representations for
matching. However, such methods cannot well capture the contextualized local
information in the matching process. To tackle this problem, we present a new
deep architecture to match two sentences with multiple positional sentence
representations. Specifically, each positional sentence representation is a
sentence representation at this position, generated by a bidirectional long
short term memory (Bi-LSTM). The matching score is finally produced by
aggregating interactions between these different positional sentence
representations, through -Max pooling and a multi-layer perceptron. Our
model has several advantages: (1) By using Bi-LSTM, rich context of the whole
sentence is leveraged to capture the contextualized local information in each
positional sentence representation; (2) By matching with multiple positional
sentence representations, it is flexible to aggregate different important
contextualized local information in a sentence to support the matching; (3)
Experiments on different tasks such as question answering and sentence
completion demonstrate the superiority of our model.Comment: Accepted by AAAI-201
Learning Visual Features from Snapshots for Web Search
When applying learning to rank algorithms to Web search, a large number of
features are usually designed to capture the relevance signals. Most of these
features are computed based on the extracted textual elements, link analysis,
and user logs. However, Web pages are not solely linked texts, but have
structured layout organizing a large variety of elements in different styles.
Such layout itself can convey useful visual information, indicating the
relevance of a Web page. For example, the query-independent layout (i.e., raw
page layout) can help identify the page quality, while the query-dependent
layout (i.e., page rendered with matched query words) can further tell rich
structural information (e.g., size, position and proximity) of the matching
signals. However, such visual information of layout has been seldom utilized in
Web search in the past. In this work, we propose to learn rich visual features
automatically from the layout of Web pages (i.e., Web page snapshots) for
relevance ranking. Both query-independent and query-dependent snapshots are
considered as the new inputs. We then propose a novel visual perception model
inspired by human's visual search behaviors on page viewing to extract the
visual features. This model can be learned end-to-end together with traditional
human-crafted features. We also show that such visual features can be
efficiently acquired in the online setting with an extended inverted indexing
scheme. Experiments on benchmark collections demonstrate that learning visual
features from Web page snapshots can significantly improve the performance of
relevance ranking in ad-hoc Web retrieval tasks.Comment: CIKM 201
Comparative analysis of differential gene expression in two species of crucian carps in response to Cyprinid herpesvirus 2 (CyHV-2) infection
We assessed the expressions of MHCI, LYZC, keratin8, MPO, DUSP1, IκBα, Rab21, and Rac2 between two species of carps (Erqisi river crucian carp and allogynogenetic crucian carp) after Cyprinid herpesvirus 2 (CyHV-2) infection. The relative expressions of MHCI, LYZC, and keratin8 in the virus-challenged groups were significantly higher than control groups. Moreover, the expression of IκBα in the virus-challenged groups was significantly lower than in the control groups. Compared with the virus-challenged ERO group, the expression of IκBα in the virus-challenged ZHO group decreased. The expression of Rab21 in the virus-challenged groups gradually increased and was significantly higher than in the control groups, and then its expression began to decrease after 24 h. At 72 h, the expression of IκBα in both virus-challenged groups was significantly lower than in the control groups. In addition, the expression of Rab21 in the virus-challenged ZHO group was significantly higher than the virus-challenged ERO group at all time points except for 72 h. Before 24 h, the expression of Rac2 remained unchanged in these four groups, and its expression in the virus-challenged ZHO group was significantly higher than in the other three groups. Nevertheless, its expression began to decrease after 24 h but was still slightly higher than the control group at 72 h. MPO showed a similar expression pattern as Rac2. The expression of DUSP1 in the four groups was the same at 0 h. However, its expression in the virus-challenged ZHO group was significantly higher than in the other three groups at other time points
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