Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions
Textual definitions constitute a fundamental source of knowledge when seeking the meaning of words, and they are the cornerstone of lexical resources like glossaries, dictionaries, encyclopedia or thesauri. In this paper, we present an in-depth analytical study on the main features relevant to the task of definition extraction. Our main goal is to study whether linguistic structures from canonical (the Aristotelian or genus et differentia model) can be leveraged to retrieve definitions from corpora in different domains of knowledge and textual genres alike. To this end, we develop a simple linear classifier and analyze the contribution of several (sets of) linguistic features. Finally, as a result of our experiments, we also shed light on the particularities of existing benchmarks as well as the most challenging aspects of the task.
Code (0)
등록된 구현이 없습니다.
Tasks
Definition ExtractionSimilar Papers 제목 키워드 기반
Higher-Order Ambiguity Attitudes
We introduce a model-free preference under ambiguity, as a primitive trait of behavior, which we apply once as well as repeatedly. Its single and double application yield simple, easily interpretable definitions of ambig…
Defx at SemEval-2020 Task 6: Joint Extraction of Concepts and Relations for Definition Extraction
Definition Extraction systems are a valuable knowledge source for both humans and algorithms. In this paper we describe our submissions to the DeftEval shared task (SemEval-2020 Task 6), which is evaluated on an English …
Definition ExtractionExtract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction
In this work, we are interested in automated methods for knowledge graph creation (KGC) from input text. Progress on large language models (LLMs) has prompted a series of recent works applying them to KGC, e.g., via zero…
graph constructionOpen Information ExtractionRetrieval-augmented GenerationDeep Dynamic Probabilistic Canonical Correlation Analysis
This paper presents Deep Dynamic Probabilistic Canonical Correlation Analysis (D2PCCA), a model that integrates deep learning with probabilistic modeling to analyze nonlinear dynamical systems. Building on the probabilis…
Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction
Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, howe…
Dimensionality ReductionMULTI-VIEW LEARNING