Properties of corpora, such as the diversity of vocabulary and how tightly related texts cluster together, impact the best way to cluster short texts. We examine several such properties in a variety of corpora and track their effects on various combinations of similarity metrics and clustering algorithms. We show that semantic similarity metrics outperform traditional n-gram and dependency simi...

background: finding the right word is a necessity in communication, and its evaluation has always been a challenging clinical issue, suggesting the need for valid and reliable measurements. the homophone meaning generation test (hmgt) can measure the ability to switch between verbal concepts, which is required in word retrieval. the purpose of this study was to adapt and validate the persian ve...

This paper provides a novel Web image clustering methodology based on their associated texts. In our approach, the semantics of Web images are firstly represented into vectors of term-weight pairs. In order to correctly correlate terms to a Web image, the associated text of the Web image is partitioned into semantic blocks according to the semantic structure of the text with respect to the Web ...

We propose a new approach to cluster the web pages. Utilizing an iterative reinforced algorithm, the model extracts semantic feature vectors from user click-through data. We then use LSA (Latent Semantic Analysis) to reduce the feature dimension and K-means algorithm to cluster documents. Compared to the traditional way of feature extraction (lexical binomial model), our new model has better pu...

The increasing availability of heterogeneous XML informative sources has raised a number of issues concerning how to represent and manage semistructured data. Although XML sources can exhibit proper structures and contents, differently annotated XML documents may in principle encode related semantics due to subjective definitions of markup tags. Discovering knowledge to infer semantic organizat...

A Chinese character embedded in different compound words may carry different meanings. In this paper, we aim at semantic clustering of a given family of morphologically related Chinese words. In Experiment 1, we employed linguistic features at the word, syntactic, semantic, and contextual levels in aggregated computational linguistics methods to handle the clustering task. In Experiment 2, we r...

One of the key issues in both natural language understanding and generation is the appropriate processing of Multiword Expressions (MWEs). MWEs pose a huge problem to the precise language processing due to their idiosyncratic nature and diversity in lexical, syntactical and semantic properties. The semantic of a MWE cannot be expressed after combining the semantic of its constituents. Therefore...

Short texts usually encounter data sparsity and ambiguity problems in representations for their lack of context. In this paper, we propose a novel method to model short texts based on semantic clustering and convolutional neural network. Particularly, we first discover semantic cliques in embedding spaces by a fast clustering algorithm. Then, multi-scale semantic units are detected under the su...

In a document representation model the Semanti based Similarity Measure (SBSM), is proposed. This model combines phrases analysis as well as words analysis with the use of propbank notation as background knowledge to explore better ways of documents representation for clustering. The SBSM assigns semantic weights to both document words and phrases. The new weights reflect the semantic relatedne...