The search interface is made of three sections: Search, Explore, and Results. These are described in detail below.
You may start searching either from the Search section or from the Explore section.
Search
This section shows your current search criteria and allows you to submit keywords to search in the bibliography.
Each new submission adds the entered keywords to the list of search criteria.
To start a new search instead of adding keywords to the current search, use the Reset search button, then enter your new keywords.
To replace an already submitted keyword, first remove it by unchecking its checkbox, then submit a new keyword.
You may scope the search by selecting which fields to search, for instance:
In any field: Finds entries where any of the available fields contains your keywords. This is the default option.
In authors/contributors: Finds entries where any of its authors' or contributors' names contains your keywords.
In titles: Finds entries whose title contains your keywords.
You may use boolean operators with your keywords. For instance:
AND: Finds entries that contain all specified terms. This is the default relation between terms when no operator is specified, e.g., a b is the same as a AND b.
OR: Finds entries that contain any of the specified terms, e.g., a OR b.
NOT: Excludes entries that contain the specified terms, e.g., NOT a.
Boolean operators must be entered in UPPERCASE.
You may use logical groupings (with parentheses) to eliminate ambiguities when using multiple boolean operators, e.g., (a OR b) AND c.
You may require exact sequences of words (with double quotes), e.g., "a b c". The default difference between word positions is 1, meaning that an entry will match if it contains the words next to each other, but a different maximum distance may be specified (with the tilde character), e.g., "web search"~2 allows up to 1 word between web and search, meaning it could match web site search as well as web search.
You may specify that some words are more important than others (with the caret), e.g., faceted^2 search browsing^0.5 specifies that faceted is twice as important as search when computing the relevance score of the results, while browsing is half as important. Such term boosting may be applied to a logical grouping, e.g., (a b)^3 c.
Keyword search is case-insentitive, accents are folded, and punctuation is ignored.
Stemming is performed on terms from most text fields, e.g., title, abstract, notes. Words are thus reduced to their root form, saving you from having to specify all variants of a word when searching, e.g., terms such as search, searches, and searching all produce the same results. Stemming is not applied to text in name fields, e.g., authors/contributors, publisher, publication.
Explore
This section allows you to explore categories associated with the references.
Check a category to add it to your search criteria and narrow your search. The results will only show entries that are associated with the category, and more specific terms may appear under the newly selected category.
Uncheck a category to remove it from your search criteria and broaden your search results.
The numbers shown next to the categories indicate how many entries are associated with each category in the current set of results. Those numbers will vary based on your search criteria to always describe what's in the current set of results. Likewise, categories and whole facets will disappear when the result set has no entry associated to them.
Results
This section shows the search results. When no search criteria has been given, it shows the full content of the bibliography (up to 20 entries per page).
Each entry of the results list is a link to its full bibliographic record. From the bibliographic record view, you may continue exploring the search results by going to previous or following records in your search results, or you may return to the list of results.
If a Read button appears next to a result, it indicates that one or more documents are available. You may either use the button for direct access to the documents, or access them from the bibliographic record view.
The Abstracts button lets you toggle the display of abstracts within the list of search results. Enabling abstracts, however, will have no effect on results for which no abstract is available.
Various options are provided to let you sort the search results. One of them is the Relevance option, which ranks the results from most relevant to least relevant. The score used for ranking takes into account word frequencies as well as the fields where they appear. For instance, if a search term occurs frequently in an entry or is one of very few terms used in that entry, that entry will probably rank higher than another where the search term occurs less frequently or where lots of other words also occur. Likewise, a search term will have more effect on the scores if it is rare in the whole bibliography than if it is very common. Also, if a search term appears in, e.g., the title of an entry, it will have more effect on the score of that entry than if it appeared in a less important field such as the abstract.
The Relevance sort is only available after keywords have been submitted using the Search section.
Categories selected in the Explore section have no effect on the relevance score. Their only effect is to filter the list of results.
Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.