Unsupervised Topic-Specific Domain Dependency Graphs for Aspect Identification in Sentiment Analysis
Code (0)
등록된 구현이 없습니다.
Tasks
Aspect-Based Sentiment Analysis (ABSA)Information RetrievalOpinion MiningSentiment AnalysisText SummarizationSimilar Papers 제목 키워드 기반
A Survey of Unsupervised Dependency Parsing
Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse trees. Des…
Dependency ParsingSurveyUnsupervised Dependency ParsingUnsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion
We advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs. Starting from well-founded assumptions about word graphs, we present simple but reliable …
Abstractive Dialogue SummarizationRerankingSentenceSentence CompressionImproving Topic Segmentation by Injecting Discourse Dependencies
Recent neural supervised topic segmentation models achieve distinguished superior effectiveness over unsupervised methods, with the availability of large-scale training corpora sampled from Wikipedia. These models may, h…
SegmentationSentenceTopic Modeling Revisited: A Document Graph-based Neural Network Perspective
Most topic modeling approaches are based on the bag-of-words assumption, where each word is required to be conditionally independent in the same document. As a result, both of the generative story and the topic formulati…
Variational InferenceTreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific Domain
Word sense disambiguation (WSD) is one of the main challenges in Computational Linguistics. TreeMatch is a WSD system originally developed using data from SemEval 2007 Task 7 (Coarse-grained English All-words Task) that …
Word Sense Disambiguation