Sentence examples for distributional representations from inspiring English sources

Both types of word representation features (clustering-based and distributional representations) improved the performance of ML-based NER systems.

The focus of this study was then to compare the contribution of two types of word representation features: clustering-based and distributional representations.

According to a review by Turian et al. [ 20], WR features can be divided into three categories: (1) clustering-based methods such as Brown clustering [ 27]; (2) distributional representations, such as LSA [ 23], LDA [ 24], and random indexing [ 25]; and (3) word embeddings (also called distributed representations), such as neural language models [ 28].

The projection matrix E∈ ℜ|V|×H maps each word to the feature vector as the distributional representation and fed into the hidden layer.

The coarsening kinetics are determined by adding two global constraints on the distributional representation of the microstructure, which specify the volume fraction and critical (zero-growth) particle size in terms of explicit spatial and ensemble averages.

Furthermore, we extracted two different types of word representation features (clustering-based representation features and distributional representation features) and integrated them with the SSVMs-based clinical NER system.

In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings.

In this paper, we investigated the effect of three types of WR features, including clustering-based representation, distributional representation, and word embeddings, on machine learning-based BNER systems.

This distributional representation thus aggregates the individuals into role classes, but takes into account the contact heterogeneities and the sparseness of the contact network.

It is not surprising that word representation features such as clustering-based and distributional word representations improved performance of clinical NER systems, as it was reported by previous studies as well [ 16].

In this study, we investigated the use of SSVMs and clustering-based and distributional word representations for clinical entity recognition.