SemEval-2017 Task-10

A SemEval-2017 Task-10 is a SemEval-2017 task dealt with automatic extraction of keyphrases from scientific publications, as well as extracting types of keyphrases and relations between keyphrases.

• AKA: ScienceIE-2017.
• Context:
  • It can be composed of:
    • ScienceIE-2017 Training Data[1]
    • ScienceIE-2017 Development Data[2]
    • ScienceIE-2017 Scripts[3]
    • ScienceIE-2017 Annotation Guidelines[4]
• Example(s):
  • ScienceIE-2017(“Information extraction is the process of extracting structured data from unstructured text, which is relevant for several end-to-end tasks, including question answering. This paper addresses the tasks of named entity recognition (NER), a subtask of information extraction, using conditional random fields (CRF). Our method is evaluated on the ConLL-2003 NER corpus.”)
    ⇒ “Information extraction[0-TASK] is the process of extracting structured data from unstructured text, which is relevant for several end-to-end tasks, including question answering[1-TASK]. This paper addresses the tasks of named entity recognition[2-TASK] (NER[3-TASK]), a subtask of information extraction[4-TASK], using conditional random fields[5-PROCESS] (CRF[6-PROCESS]). Our method is evaluated on the ConLL-2003 NER corpus[7-MATERIAL].” {HYPONYM-OF(2,0); SYNONYM-OF(2,3); SYNONYM-OF(5, 6). http://scienceie.github.io/example.html
• Counter-Example(s):
  • PPLRE Project.
  • SDOI Project.
• See: SemEval-2017, Synonym Extraction, Hypernym Extraction, Hyponym Extraction.

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References

• http://alt.qcri.org/semeval2017/task10/
• http://scienceie.github.io

2016

• https://scienceie.github.io/
  • QUOTE: The shared task ScienceIE at SemEval 2017 deals with automatic extraction of keyphrases from scientific publications, as well as extracting types of keyphrases and relations between keyphrases. PROCESS, TASK and MATERIAL form the fundamental objects in scientific works. Scientific research and practice is founded upon gaining, maintaining and understanding the body of existing scientific work in specific areas related to such fundamental objects. Some typical questions, researchers and practitioners more than often face are:
    • which papers have addressed a specific TASK ?
    • which papers have studied a PROCESS or variants ?
    • which papers have utilized such MATERIALS ?
    • which papers have addressed this TASK using variants of this PROCESS ?
  • Review papers are seldomly available in most research areas, and ability of search engines for scientific publications is limited. In addition to this, researchers often only have a vague search requirements which makes it hard to answer the above questions efficiently.
  • Automatically extracting keyphrases of the scientific documents, then labelling them and extracting relationships between them can address the above questions efficiently. This will further provide utilities that can recommend relevant article to readers, match reviewers to submissions and help to explore huge collections of papers.

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