Kanopy: Analysing the Semantic Network around Document Topics
نویسندگان
چکیده
External knowledge bases, both generic and domain specific, available on the Web of Data have the potential of enriching the content of text documents with structured information. We present the Kanopy system that makes explicit use of this potential. Besides the common task of semantic annotation of documents, Kanopy analyses the semantic network that resides in DBpedia around extracted concepts. The system’s main novelty lies in the translation of social network analysis measures to semantic networks in order to find suitable topic labels. Moreover, Kanopy extracts advanced knolwedge in the form of subgraphs that capture the relationships between the concepts.
منابع مشابه
The Topology of a Discussion: The #Occupy Case
INTRODUCTION We analyse a large sample of the Twitter activity that developed around the social movement 'Occupy Wall Street', to study the complex interactions between the human communication activity and the semantic content of a debate. METHODS We use a network approach based on the analysis of the bipartite graph @Users-#Hashtags and of its projections: the 'semantic network', whose nodes...
متن کاملEfficient semantic indexing via neural networks with dynamic supervised feedback
We describe a portable system for e cient semantic indexing of documents via neural networks with dynamic supervised feedback. We initially represent each document as a modified TF-IDF sparse vector and then apply a learned mapping to a compact embedding space. This mapping is produced by a shallow neural network which learns a latent representation for the textual graph linking words to nearby...
متن کاملA Joint Semantic Vector Representation Model for Text Clustering and Classification
Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...
متن کاملیک مدل موضوعی احتمالاتی مبتنی بر روابط محلّی واژگان در پنجره‌های هم‌پوشان
A probabilistic topic model assumes that documents are generated through a process involving topics and then tries to reverse this process, given the documents and extract topics. A topic is usually assumed to be a distribution over words. LDA is one of the first and most popular topic models introduced so far. In the document generation process assumed by LDA, each document is a distribution o...
متن کاملLearning Document-Level Semantic Properties from Free-text Annotations
This paper demonstrates a new method for leveraging unstructured annotations to infer semantic document properties. We consider the domain of product reviews, which are often annotated by their authors with free-text keyphrases, such as “a real bargain” or “good value.” We leverage these unstructured annotations by clustering them into semantic properties, and then tying the induced clusters to...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013