Semantic relatedness measures quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relate...

This work introduces methodologies for extending the modelling and querying capabilities of Trajectories Data Warehouses (TDW) in the context of semantic trajectories. Specifically, we incorporate the notion of Semantic Relatedness (SR) as part of the formal model of a TDW, which enables capturing the similarity between different annotations describing Points of Interest (POI), locations and ac...

Distributional measures of semantic relatedness determine word similarity based on how frequently a pair of words appear in the same contexts. A typical method is to construct a word-context matrix, then re-weight it using some measure of association, and finally take the vector distance as a measure of similarity. This has largely been an unsupervised process, but in recent years more work has...

Measurement of words semantic relatedness plays an important role in a wide range of natural language processing and information retrieval applications, such as full-text search, summarization, classification and clustering. In this paper, we propose an easy to implement and low-cost method for estimating words semantic relatedness. The proposed method is based on the utilization of words tempo...

when emerging technologies such as search engine marketing (sem) face tasks that require human level intelligence, it is inevitable to use the knowledge repositories to endow the machine with the breadth of knowledge available to humans. keyword suggestion for search engine advertising is an important problem for sponsored search and sem that requires a goldmine repository of knowledge. a recen...

Wikipedia provides a semantic network for computing semantic relatedness in a more structured fashion than a search engine and with more coverage than WordNet. We present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts,...

Determining semantic relatedness between words or concepts is a fundamental process to many Natural Language Processing applications. Approaches for this task typically make use of knowledge resources such as WordNet and Wikipedia. However, these approaches only make use of limited number of features extracted from these resources, without investigating the usefulness of combining various diffe...

The automatic ranking of word pairs as per their semantic relatedness and ability to mimic human notions of semantic relatedness has widespread applications. Measures that rely on raw data (distributional measures) and those that use knowledge-rich ontologies both exist. Although extensive studies have been performed to compare ontological measures with human judgment, the distributional measur...

We present a new method for computing semantic relatedness of concepts. The method relies solely on the structure of a conceptual network and eliminates the need for performing additional corpus analysis. The network structure is employed to generate artificial conceptual glosses. They replace textual definitions proper written by humans and are processed by a dictionary based metric of semanti...

Previous work on text coherence was primarily based on matching multiple mentions of the same entity in different parts of the text; therefore, it misses the contribution from semantically related but not necessarily coreferential entities (e.g., Gates and Microsoft). In this paper, we capture such semantic relatedness by leveraging world knowledge (e.g., Gates is the person who created Microso...