Automatic language processing systems depend on, among others factors, the effectiveness in modeling human cognitive abilities, including the capacity to draw inferences about prototypical or expected sequences of events and their temporal order. Appropriate response to a crisis is as important for public security as are efforts to prevent any such natural or man made disaster. Recent research ...

In the past decades, kernel methods are enthusiastically explored for relation extraction. This paper proposes a penalty tree similarity algorithm by extending the dependency tree kernel. Dependency tree kernel computes the similarity of two parse trees by enumerating their matched sub-trees. The penalty tree similarity, however, not only consider the similar structures of the parse trees, but ...

In this paper, we describe the system submitted by the team TUD to Task 8 at SemEval 2010. The challenge focused on the identification of semantic relations between pairs of nominals in sentences collected from the web. We applied maximum entropy classification using both lexical and syntactic features to describe the nominals and their context. In addition, we experimented with features descri...

Relation extraction is a subtask of information extraction that aims at obtaining instances of semantic relations present in texts. This information can be arranged in machine-readable formats, useful for several applications that need structured semantic knowledge. The work presented in this paper explores different strategies to automate the extraction of semantic relations from texts in Port...

We report results of experiments which build and refine models of rhetoricalsemantic relations such as Cause and Contrast. We adopt the approach of Marcu and Echihabi (2002), using a small set of patterns to build relation models, and extend their work by refining the training and classification process using parameter optimization, topic segmentation and syntactic parsing. Using human-annotate...

This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in semantic relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually predefined or automatically clustered, a discriminative function is determined in a top-down way. As the upper-level class normally has much more positive training ex...

We present an approach for ontology driven extraction of relations from texts aimed mainly to produce enriched semantic annotations for the Semantic Web. The approach exploits linguistic and empirical strategies, by means of a pipeline method involving processes such as a parser, part-of-speech tagger, named entity recognition system, and pattern-based classification, and resources including on...

One of the major problems for semantic search at Web scale is that the search results on the semantic data might be huge and the users have to browse to find the most relevant ones. Plus, due to the reason for the context, user requirement may diverse even the input query may be the same. In this paper, we try to achieve scalability in semantic search through social relation diversity of differ...

Several Semantic Web specific tasks such as ontology learning/extension or ontology matching rely on identifying relations between two given concepts. Scarlet is a technique for discovering relations between two given concepts by exploring ontologies available on the Semantic Web as a source of background knowledge. By relying on semantic web search engines such as Watson, Scarlet automatically...

Nowadays we are facing a growing demand for semantic knowledge in computational applications, particularly in Natural Language Processing (NLP). However, there aren’t sufficient human resources to produce that knowledge at the same rate of its demand. Considering the Portuguese language, which has few resources in the semantic area, the situation is even more alarming. Aiming to solve that prob...