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First year of the UQ sustainable energy micromasters series: Evaluation of participation and achievement
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Leveraging knowledge graph embeddings for natural language question answering
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Using LIP to gloss over faces in single-stage face detection networks
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Vertical and sequential sentiment analysis of micro-blog topic
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DAVE: extracting domain attributes and values from text corpus
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Mining the context of citations in scientific publications
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Abstract:
Recent advancements in information retrieval systems significantly rely on the context-based features and semantic matching techniques to provide relevant information to users from ever-growing digital libraries. Scientific communities seek to understand the implications of research, its importance and its applicability for future research directions. To mine this information, absolute citations merely fail to measure the importance of scientific literature, as a citation may have a specific context in full text. Thus, a comprehensive contextual understanding of cited references is necessary. For this purpose, numerous techniques have been proposed that tap the power of artificial intelligence models to detect important or incidental (non-important) citations in full text scholarly publications. In this paper, we compare and build upon on four state-of-the-art models that detect important citations using 450 manually annotated citations by experts - randomly selected from 20,527 papers from the Association for Computational Linguistics corpus. Of the total 64 unique features proposed by the four selected state-of-the-art models, the top 29 were chosen using the Extra-Trees classifier. These were then fed it to our supervised machine learning based models: Random Forest (RF) and Support Vector Machine. The RF model outperforms existing selected systems by more than 10%, with 89% precision-recall curve. Finally, we qualitatively assessed important and non-important citations by employing and self-organizing maps. Overall, our research work supports information retrieval algorithms that detect and fetch scientific articles on the basis of both qualitative and quantitative indices in scholarly big data.
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Keyword:
1700 Computer Science; 2614 Theoretical Computer Science; Citation context analysis; Influential citations; Machine learning; Self-organizing maps
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URL: https://espace.library.uq.edu.au/view/UQ:4ca0d2e
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Structural operational semantics through context-dependent behaviour
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A Correlation Analysis on LSA and HAL Semantic Space Models
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Fuzzy K-means clustering on a high dimensional semantic space
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A natural language interpreter for the construction of conceptual schemas
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