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 $k$-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