SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications (1704.02853v3)

Abstract: We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

• The paper evaluates methods for extracting keyphrases and semantic relations from scientific publications, demonstrating the promise of deep learning and CRF models.
• It details a three-part task—keyphrase identification, classification, and relation extraction—using a meticulously annotated dataset from multiple scientific domains.
• The findings highlight performance gaps among techniques, providing actionable benchmarks and insights for advancing NLP in academic text mining.

Extracting Keyphrases and Relations from Scientific Publications: Insights from SemEval 2017 Task 10

The paper "SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications" provides a comprehensive overview of the task focused on the extraction of keyphrases and their semantic relations from academic literature. This endeavor ties into broader efforts within the NLP field, specifically targeting mention-level keyphrase extraction, classification, and relation identification. This undertaking serves not only to facilitate advanced document navigation but also assists in organizing the vast information landscape within scientific publications.

Overview of the Task

The task at hand is dissected into three critical subtasks:

1. Mention-level Keyphrase Identification (Subtask A): This involves locating keyphrases within a document, a task complicated by the domain variability and absence of consistent signifiers.
2. Mention-level Keyphrase Classification (Subtask B): Here, keyphrases are categorized into types (\textsf{PROCESS}, \textsf{TASK}, and \textsf{MATERIAL}).
3. Mention-level Semantic Relation Extraction (Subtask C): This subtask focuses on identifying hierarchical relations between keyphrases, specifically \textsf{HYPONYM-OF} and \textsf{SYNONYM-OF} relations.

Dataset and Evaluation Scenarios

The dataset curated for this task is derived from the ScienceDirect database encompassing publications across Computer Science, Material Sciences, and Physics. This dataset underwent a meticulous manual annotation process involving double annotation by students and expert annotators to ensure high-quality data. The task evaluation was structured into three scenarios:

• Scenario 1: Receiving only the plain text necessitating solutions for Subtasks A, B, and C.
• Scenario 2: Provided with manually annotated keyphrase boundaries for Subtasks B and C.
• Scenario 3: Supplied with annotated keyphrases and types for Subtask C.

Performance and Methodologies

Evaluation evidenced that sequence-to-sequence prediction models often integrated with neural networks and CRFs performed prominently, demonstrating that keyphrase identification remains particularly challenging due to the length and uniqueness of these phrases within the corpus. Top-performing submissions such as those from s2_end2end leveraged RNNs, sometimes augmented with CRF layers, indicating a trend towards deep learning methodologies for these types of NLP tasks.

Implications and Future Directions

The results and insights from the SemEval 2017 Task 10 underscore the computational complexities inherent in extracting term-level and relational-level information from scientific literature, which has profound implications for the development of AI systems in automated academic curation and retrieval tasks. The performance disparities between various approaches highlight both the promise and the limitations of current NLP techniques.

Future research could explore enhancing the generalization capabilities of these models, especially in domains with diverse and novel vocabulary. Moreover, leveraging large-scale, pretrained LLMs could potentially augment the quality of keyphrase identification and relationship extraction, fostering more robust and scalable solutions in scientific text mining.

In conclusion, this SemEval task provides valuable benchmarks and insights, paving the way for more sophisticated methods in the field of scientific text processing. The annotated dataset and findings here offer a substantial contribution to NLP researchers focusing on keyphrase extraction and semantic relationships in technical texts.