We describe the systems of NLP-CIC team that participated in the Complex Word Identification (CWI) 2018 shared task. The shared task aimed to benchmark approaches for identifying complex words in English and other languages from the perspective of non-native speakers. Our goal is to compare two...
Rationale of this Turing-like evaluation could be applied to many other NLP complex tasks like Machine translation or Text Generation. We show that a state of the art ASC system can pass such a test and simulate a human summary in 60\% of the cases. 展开 ...
lexical simplification: addressing words and short phrases. explanation generation: addressing word meanings. [Related article: An Introduction to Natural Language Processing (NLP)] The Approach Building a text simplification program begins with a primary pipeline. It starts with pre-processing raw text ...
(NLP); thus, they proposed an automated rule transformation method consisting of (1) information extraction, which recognizes and labels words and phrases in relevant sentences with predefined tags, and (2) information transformation, which transforms the extracted information instances into logic clause...
sheffieldnlp/cwi • NAACL 2019 Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a target audience.1 Paper Code Complex Word Identification as a Sequence Labelling Task siangooding/cwi • • ACL 2019 Complex ...
The topic model uses the text information in the knowledge base to learn the common entity group and realize the entity collective disambiguation [84]. Another disambiguation approach is based on the context and semantic similarity of entity information words in knowledge graphs [85]. In addition,...
In Table 1, the main effect of gender shows that men use, on average, longer and more general and abstract words. For frequency, significance was not obtained. Women, on the other hand, score higher on lexical diversity. Effect sizes are comparable, hovering around 0.5, expressed in standard...
When used in input of machine learning algorithms, embedding vectors help with common graph problems such as link prediction, graph matching, etc In Natural Language Processing (NLP), such a vectorization process is also employed. Word embedding has the goal of representing the sense of words, ...
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uses the meaning of a search query to compare it to candidate objects.Natural language processing(NLP) models convert text and whole documents into vector embeddings. These models seek to represent the context of words and the meaning they convey. Users can then query using natural language and ...