This report describes the UNITN system, a Part-Of-Speech Context Counter, that participated at Semeval 2010 Task 8: MultiWay Classification of Semantic Relations Between Pairs of Nominals. Given a text annotated with Part-of-Speech, the system outputs a vector representation of a sentence containing 20 features in total. There are three steps in the system’s pipeline: first the system produces ...

In translation, choosing appropriate equivalent is essential to convey the right message from source-text to target-text, and one of the issues that may have a determinative role in appropriate equivalent choice is the semantic prosody (SP) behavior of words and the relation existing between the SP of a word and semantic senses (i.e. negativity, positivity or neutrality) of its collocations in ...

This paper describes our system for SemEval-2010 Task 8 on multi-way classification of semantic relations between nominals. First, the type of semantic relation is classified. Then a relation typespecific classifier determines the relation direction. Classification is performed using SVM classifiers and a number of features that capture the context, semantic role affiliation, and possible pre-e...

Facing the challenges of annotating naturally occurring text into semantic structured form for automatically information extracting, current Semantic Role Labeling (SRL) systems have been focusing on semantic predicateargument structure. Based on the Concept Description Language for Natural Language (CDL.nl) which aims to describe the concept structure of text by a set of pre-defined semantic r...

Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity sentences, some important contextual information can guide the semantic representation of improve accuracy relation extraction. However, most existing models ignore guidance and treat textual context sentence equally. This results low-accuracy extractions. To add...

Automatically-derived grammars, such as the split-and-merge model, have proven helpful in parsing (Petrov et al., 2006). As such grammars are refined, latent information is recovered which may be usable for linguistic tasks besides parsing. In this paper, we present and examine a new method of semantic relation classification: using automaticallyderived grammar rule clusters as a robust knowled...