Implicit Dimension Identification in User-Generated Text with LSTM Networks
In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative. It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers. We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user's knowledge, intent or belief that may be based on writer's moral foundation: 1) political perspective detection in news articles 2) identification of informational vs. conversational questions in community question answering (CQA) archives and. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesCommunity Question AnsweringQuestion AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Do LLMs Implicitly Determine the Suitable Text Difficulty for Users?
Education that suits the individual learning level is necessary to improve students' understanding. The first step in achieving this purpose by using large language models (LLMs) is to adjust the textual difficulty of th…
Question AnsweringMulti-use LLM Watermarking and the False Detection Problem
Digital watermarking is a promising solution for mitigating some of the risks arising from the misuse of automatically generated text. These approaches either embed non-specific watermarks to allow for the detection of a…
User IdentificationIdentification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles
A key factor to optimal acceptance and comfort of automated vehicle features is the driving style. Mismatches between the automated and the driver preferred driving styles can make users take over more frequently or even…
You Only Submit One Image to Find the Most Suitable Generative Model
Deep generative models have achieved promising results in image generation, and various generative model hubs, e.g., Hugging Face and Civitai, have been developed that enable model developers to upload models and users t…
Image GenerationText MatchingDeep neural network-based classification model for Sentiment Analysis
The growing prosperity of social networks has brought great challenges to the sentimental tendency mining of users. As more and more researchers pay attention to the sentimental tendency of online users, rich research re…
ClassificationGeneral ClassificationSentiment AnalysisSentiment Classification