SYNONYM DETECTION USING SYNTACTIC DEPENDENCY AND NEURAL EMBEDDINGS
نویسندگان
چکیده
منابع مشابه
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures
We present a simple method to learn continuous representations of dependency substructures (links), with the motivation of directly working with higher-order, structured embeddings and their hidden relationships, and also to avoid the millions of sparse, template-based word-cluster features in dependency parsing. These link embeddings allow a significantly smaller and simpler set of unary featu...
متن کاملSingleton Detection using Word Embeddings and Neural Networks
Singleton (or non-coreferential) mentions are a problem for coreference resolution systems, and identifying singletons before mentions are linked improves resolution performance. Here, a singleton detection system based on word embeddings and neural networks is presented, which achieves state-of-the-art performance (79.6% accuracy) on the CoNLL2012 shared task development set. Extrinsic evaluat...
متن کاملNeural Semantic Role Labeling with Dependency Path Embeddings
This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques. Our approach is motivated by the observation that complex syntactic structures and related phenomena, such as nested subordinations and nominal predicates, are not handled well by existing models. Our model treats such instances as subsequences of lexicalized dependency paths an...
متن کاملDependency-Based Word Embeddings
While continuous word embeddings are gaining popularity, current models are based solely on linear contexts. In this work, we generalize the skip-gram model with negative sampling introduced by Mikolov et al. to include arbitrary contexts. In particular, we perform experiments with dependency-based contexts, and show that they produce markedly different embeddings. The dependencybased embedding...
متن کاملStyle Breach Detection with Neural Sentence Embeddings
The paper investigates method for the style breach detection task. We developed a method based on mapping sentences into high dimensional vector space. Each sentence vector depends on the previous and next sentence vectors. As main architecture for this mapping we use the pre-trained encoder-decoder model. Then we use these vectors for constructing an author style function and detecting outlier...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IADIS International Journal on www/Internet
سال: 2022
ISSN: ['1645-7641']
DOI: https://doi.org/10.33965/ijwi_202220102