Theoretical Analysis of Chinese Word Embeddings’ Entailment Structure: Exploratory Approach


Theoretical Analysis of Chinese Word Embeddings’ Entailment Structure: Exploratory Approach – We present the first generalization of the language-specific dictionary for language-independent words such as phonetic and lexical expressions, with language-specific words being a special case. The dictionary has been tested on the task of recognizing Chinese word representations from a naturalistic corpus of 10,000 words, using a different classifier than the ones previously used for training English. The performance of the model on the test corpus is significantly better than the previous results on the same corpus. This is in contrast to a similar model which is able to use a separate dictionary for word representation and was applied on the same corpus. This model also can be used for word prediction.

We provide a framework for identifying and ranking a set of items with a probabilistic model in the form of a hierarchy ranking graph. The problem of ranking items is often approached in this manner, in which the model is considered as a hierarchical network. This problem has been considered in many applications such as classification, classification of biological data, and in particular clustering. We consider a simple but effective approach to this problem which allows us to identify informative items in multiple dimensions and ranks them by a hierarchical ranking graph. Using the proposed algorithm, we show that this task can be performed asynchronously and in the same manner as clustering. This is achieved by combining two methods: one based on temporal ordering of clusters and the other based on linear time delay of rank updates. We show that clustering over large hierarchical networks is computationally efficient with a high probability.

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Theoretical Analysis of Chinese Word Embeddings’ Entailment Structure: Exploratory Approach

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    Learning to rank using hierarchical clusteringWe provide a framework for identifying and ranking a set of items with a probabilistic model in the form of a hierarchy ranking graph. The problem of ranking items is often approached in this manner, in which the model is considered as a hierarchical network. This problem has been considered in many applications such as classification, classification of biological data, and in particular clustering. We consider a simple but effective approach to this problem which allows us to identify informative items in multiple dimensions and ranks them by a hierarchical ranking graph. Using the proposed algorithm, we show that this task can be performed asynchronously and in the same manner as clustering. This is achieved by combining two methods: one based on temporal ordering of clusters and the other based on linear time delay of rank updates. We show that clustering over large hierarchical networks is computationally efficient with a high probability.


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