We present a description of the system submitted to the Semantic Textual Similarity (STS) shared task at SemEval 2016. The task is to assess the degree to which two sentences carry the same meaning. We have designed two different methods to automatically compute a similarity score between sentences. The first method combines a variety of semantic similarity measures as features in a machine lea...

The problem to detect the similarity or the difference between objects are faced regularly in several domains of applications such as e-commerce, social network, expert system, data mining, decision support system, etc. This paper introduces a general model for measuring the similarity between objects based on their attributes. In this model, the similarity on each attribute is defined with dif...

This work demonstrates how the World Wide Web can be mined in a fully automated manner for discovering the semantic similarity relationships among the concepts surfaced during an electronic brainstorming session, and thus improving the accuracy of automated clustering meeting messages. Our novel Context Sensitive Similarity Discovery (CSSD) method takes advantage of the meeting context when sel...

The World Wide Web is growing rapidly and many search engines do not cover all the visible pages. Therefore, a more effective crawling method is required to collect more accurate data. In this paper, we introduce an effective focused web crawler containing smart methods. In text analysis, similarity measurement applies to different parts of the Web pages including title, body, anchor text and U...

In this paper, effective Chinese definite anaphora resolution is addressed by using feature weight learning and Web-based knowledge acquisition. The presented salience measurement is based on entropybased weighting on selecting antecedent candidates. The knowledge acquisition model is aimed to extract more semantic features, such as gender, number, and semantic compatibility by employing multip...

Classifying and mining noise-free web pages will improve on accuracy of search results as well as search speed, and may benefit webpage organization applications (e.g., keyword-based search engines and taxonomic web page categorization applications). Noise on web pages are irrelevant to the main content on the web pages being mined, and include advertisements, navigation bar, and copyright noti...

Computing semantic similarity between two terms is essential for a variety of text analytics and understanding applications. However, existing approaches are more suitable for semantic similarity between words rather than the more general multi-word expressions (MWEs), and they do not scale very well. Therefore, we propose a lightweight and effective approach for semantic similarity using a lar...

This study proposed a new method about clustering in documents. Clustering is a very powerful data mining technique for topic discovery from documents. In document clustering, it must be more similarity between intra-document and less similarity between intra-document of two clusters. The cosine function measures the similarity of two documents. When the clusters are not well separated, partiti...

We present WebSim (Web-based Similarity metric), whose feature extraction and similarity model is based on a conventional Web search engine. By utilizing the search engine, we can obtain the freshest content for each term that represents the up-to-date knowledge on the term. In comparison with previous text mining approaches that use the certain amount of crawled Web documents as corpus, our me...