Research on Annotation Rules and Recognition Algorithm Based on Phrase Window
At present, most Natural Language Processing technology is based on the results of Word Segmentation for Dependency Parsing, which mainly uses an end-to-end method based on supervised learning. There are two main problems with this method: firstly, the la-beling rules are complex and the data is too difficult to label, the workload of which is large; secondly, the algorithm cannot recognize the multi-granularity and diversity of language components. In order to solve these two problems, we propose labeling rules based on phrase windows, and designed corresponding phrase recognition algorithms. The labeling rule uses phrases as the minimum unit, di-vides sentences into 7 types of nestable phrase types, and marks the grammatical dependencies between phrases. The corresponding algorithm, drawing on the idea of identifying the target area in the image field, can find the start and end positions of various phrases in the sentence, and realize the synchronous recognition of nested phrases and grammatical dependencies. The results of the experiment shows that the labeling rule is convenient and easy to use, and there is no ambiguity; the algorithm is more grammatically multi-granular and diverse than the end-to-end algorithm. Experiments on the CPWD dataset improve the accuracy of the end-to-end method by about 1 point. The corresponding method was applied to the CCL2018 competition, and the first place in the Chinese Metaphor Sentiment Analysis Task.
Code (0)
등록된 구현이 없습니다.
Tasks
Dependency ParsingSentenceSentiment AnalysisSimilar Papers 제목 키워드 기반
Research on multi-dimensional end-to-end phrase recognition algorithm based on background knowledge
At present, the deep end-to-end method based on supervised learning is used in entity recognition and dependency analysis. There are two problems in this method: firstly, background knowledge cannot be introduced; second…
Algorithms For Automatic Accentuation And Transcription Of Russian Texts In Speech Recognition Systems
This paper presents an overview of rule-based system for automatic accentuation and phonemic transcription of Russian texts for speech connected tasks, such as Automatic Speech Recognition (ASR). Two parts of the develop…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech Recognition+2Improving Neural Language Models by Segmenting, Attending, and Predicting the Future
Common language models typically predict the next word given the context. In this work, we propose a method that improves language modeling by learning to align the given context and the following phrase. The model does …
Language ModelingLanguage ModellingSegmentationPredicting Phrase Breaks in Classical and Modern Standard Arabic Text
We train and test two probabilistic taggers for Arabic phrase break prediction on a purpose-built, gold standard, boundary-annotated and PoS-tagged Qur'an corpus of 77430 words and 8230 sentences. In a related LREC pap…
ChunkingHuman ParsingPart-Of-Speech TaggingPOS+1GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules. However, devising labeling rules is challenging because it often re…
Graph Neural Networknamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2