This paper discusses the application of the Expectation-Maximization (EM) clustering algorithm to the task of Chinese verb sense discrimination. The model utilized rich linguistic features that capture predicateargument structure information of the target verbs. A semantic taxonomy for Chinese nouns, which was built semi-automatically based on two electronic Chinese semantic dictionaries, was u...

Text clustering has been recognized as an important component in data mining. Self-Organizing Map (SOM) based models have been found to have certain advantages for clustering sizeable text data. However, current existing approaches lack in providing an adaptive hierarchical structure within in a single model. This paper presents a new method of hierarchical text clustering based on combination ...

Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We...

We propose two novel zero-shot learning methods for semantic utterance classification (SUC) using deep learning. Both approaches rely on learning deep semantic embeddings from a large amount of Query Click Log data obtained from a search engine. Traditional semantic utterance classification systems require large amounts of labelled data, whereas our proposed methods make use of the structure of...

In this paper, we propose a soft-decision, unsupervised clustering algorithm that generates semantic classes automatically using the probability of class membership for each word, rather than deterministically assigning a word to a semantic class. Semantic classes are induced using an unsupervised, automatic procedure that uses a context-based similarity distance to measure semantic similarity ...

Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term ...

Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term ...

Jointly performing semantic and instance segmentation of 3D point cloud remains a challenging task. In this work, novel framework called joint semantic-instance via multi-scale Semantic Association Salient clustering Optimization was proposed to tackle problem. Inspired by the inherent correlation among objects in space, Multi-scale (MSA) module explore constructive effect context information f...