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Found 18 papers, 11 papers with code
Prompt-tuning for Clickbait Detection via Text Summarization
To address this problem, we propose a prompt-tuning method for clickbait detection via text summarization in this paper, text summarization is introduced to summarize the contents, and clickbait detection is performed based on the similarity between the generated summary and the contents.
Multilingual Lexical Simplification via Paraphrase Generation
After feeding the input sentence into the encoder of paraphrase modeling, we generate the substitutes based on a novel decoding strategy that concentrates solely on the lexical variations of the complex word.
Clickbait Detection via Large Language Models
Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as news portals and social media.
Sentence Simplification Using Paraphrase Corpus for Initialization
We train three different neural SS methods with our initialization, which can obtain substantial improvements on the available WikiLarge data compared with themselves without initialization.
ParaLS: Lexical Substitution via Pretrained Paraphraser
Lexical substitution (LS) aims at finding appropriate substitutes for a target word in a sentence.
Sentence Simplification via Large Language Models
Sentence Simplification aims to rephrase complex sentences into simpler sentences while retaining original meaning.
Chinese Idiom Paraphrasing
Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters.
Prompt-Learning for Short Text Classification
In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks.
Learning Canonical F-Correlation Projection for Compact Multiview Representation
With this framework as a tool, we propose a correlative covariation projection (CCP) method by using an explicit nonlinear mapping.
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages
The availability of parallel sentence simplification (SS) is scarce for neural SS modelings.
Chinese Lexical Simplification
Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning.
LSBert: A Simple Framework for Lexical Simplification
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning, to simplify the sentence.
Lexical Simplification with Pretrained Encoders
Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning.
A simple and effective postprocessing method for image classification
Whether it is computer vision, natural language processing or speech recognition, the essence of these applications is to obtain powerful feature representations that make downstream applications completion more efficient.
Improving Neural Text Simplification Model with Simplified Corpora
We train encoder-decoder model using synthetic sentence pairs and original sentence pairs, which can obtain substantial improvements on the available WikiLarge data and WikiSmall data compared with the state-of-the-art methods.
STTM: A Tool for Short Text Topic Modeling
Along with the emergence and popularity of social communications on the Internet, topic discovery from short texts becomes fundamental to many applications that require semantic understanding of textual content.
Information Retrieval
Topic Modeling over Short Texts by Incorporating Word Embeddings
Inferring topics from the overwhelming amount of short texts becomes a critical but challenging task for many content analysis tasks, such as content charactering, user interest profiling, and emerging topic detecting.
An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification
Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning.
