Because Size Does Matter: The Hamburg Dependency Treebank
We present the Hamburg Dependency Treebank (HDT), which to our knowledge is the largest dependency treebank currently available. It consists of genuine dependency annotations, i. e. they have not been transformed from phrase structures. We explore characteristics of the treebank and compare it against others. To exemplify the benefit of large dependency treebanks, we evaluate different parsers on the HDT. In addition, a set of tools will be described which help working with and searching in the treebank.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
HAMburger: Accelerating LLM Inference via Token Smashing
The growing demand for efficient Large Language Model (LLM) inference requires a holistic optimization on algorithms, systems, and hardware. However, very few works have fundamentally changed the generation pattern: each…
Large Language ModelBecause Syntax Does Matter: Improving Predicate-Argument Structures Parsing with Syntactic Features
Size Doesn't Matter: Cosine-Scored Sparse Autoencoders
Sparse autoencoders (SAEs) detect features via inner product, so a feature's activation scales with both its directional alignment and the input's norm. Features that fire on token norm therefore claim dictionary slots r…
Does Size Matter? Text and Grammar Revision for Parsing Social Media Data
GPU-Based Fuzzy C-Means Clustering Algorithm for Image Segmentation
In this paper, a fast and practical GPU-based implementation of Fuzzy C-Means(FCM) clustering algorithm for image segmentation is proposed. First, an extensive analysis is conducted to study the dependency among the imag…
ClusteringGPUImage SegmentationSemantic Segmentation