Eliminating Fuzzy Duplicates in Crowdsourced Lexical Resources
Collaboratively created lexical resources is a trending approach to creating high quality thesauri in a short time span at a remarkably low price. The key idea is to invite non-expert participants to express and share their knowledge with the aim of constructing a resource. However, this approach tends to be noisy and error-prone, thus making data cleansing a highly topical task to perform. In this paper, we study different techniques for synset deduplication including machine- and crowd-based ones. Eventually, we put forward an approach that can solve the deduplication problem fully automatically, with the quality comparable to the expert-based approach.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Morphological parsing of Swahili using crowdsourced lexical resources
We describe a morphological analyzer for the Swahili language, written in an extension of XFST/LEXC intended for the easy declaration of morphophonological patterns and importation of lexical resources. Our analyzer was …
Machine TranslationDiscovering Fuzzy Synsets from the Redundancy in Different Lexical-Semantic Resources
Although represented as such in wordnets, word senses are not discrete. To handle word senses as fuzzy objects, we exploit the graph structure of synonymy pairs acquired from different sources to discover synsets where w…
On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications
In this paper we analyse the benefits of incorporating interval-valued fuzzy sets into the Bousi-Prolog system. A syntax, declarative semantics and im- plementation for this extension is presented and formalised. We show…
The Mosaic Memory of Large Language Models
As Large Language Models (LLMs) become widely adopted, understanding how they learn from, and memorize, training data becomes crucial. Memorization in LLMs is widely assumed to only occur as a result of sequences being r…
Inference AttackMembership Inference AttackMemorizationDated Data: Tracing Knowledge Cutoffs in Large Language Models
Released Large Language Models (LLMs) are often paired with a claimed knowledge cutoff date, or the dates at which training data was gathered. Such information is crucial for applications where the LLM must provide up to…