English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, the automatic labeling of semantic roles in a sentence is considered as a chunking task. We define a semantic chunk as the sequence of words that fills a semantic role defined in a semantic frame. It is straightforward to convert chunking into a tagging task using one of several IOB representations. Using this representation each word is tagged with I, which means that the word is inside a chunk, or with O, which means that the word is outside a chunk, or B, which means that the word is the beginning of a chunk. Tagging can also be seen as a multi-class classification problem. After recasting the multi-class problem as a number of binary-class problems, we use support vector machines to implement the binary classifiers. We explore two semantic chunking tasks. In the first task we simultaneously detect the target word and segments of semantic roles. In the second task, in addition, we label the semantic segments with their respective semantic role types. For both tasks, we present encouraging results of experiments carried out using the annotated FrameNet database.

Target word detection and semantic role chunking using support vector machines