We consider the task of predicting how literary a text is, with a gold standard from human ratings. Aside from a standard bigram baseline, we apply rich syntactic tree fragments, mined from the training set, and a series of hand-picked features. Our model is the first to distinguish degrees of highly and less literary novels using a variety of lexical and syntactic features, and explains 76.0 %...

Semantic relations between text concepts denote the core elements of lexical semantics. This paper presents a model for the automatic detection of INTENTION semantic relation. Our approach first identifies the syntactic patterns that encode intentions, then we select syntactic and semantic features for a SVM learning classifier. In conclusion, we discuss the application of INTENTION relations t...

This article proposes a method of Semantic Role Labelling for languages with no reliable deep syntactic parser and with limited corpora annotated with semantic roles. Reasonable results may be achieved with the help of shallow parsing, provided that features used for training such shallow parsers include both lexical semantic information (here: hypernymy) and syntactic information.

Odin is an information extraction framework that applies cascades of finite state automata over both surface text and syntactic dependency graphs. Support for syntactic patterns allow us to concisely define relations that are otherwise difficult to express in languages such as Common Pattern Specification Language (CPSL), which are currently limited to shallow linguistic features. The interacti...

We propose a novel data augmentation approach to enhance computational behavioral analysis using social media text. In particular, we collect a Twitter corpus of the descriptions of annoying behaviors using the #petpeeve hashtags. In the qualitative analysis, we study the language use in these tweets, with a special focus on the fine-grained categories and the geographic variation of the langua...

The UOW submissions to the Semantic Textual Similarity task at SemEval-2012 use a supervised machine learning algorithm along with features based on lexical, syntactic and semantic similarity metrics to predict the semantic equivalence between a pair of sentences. The lexical metrics are based on wordoverlap. A shallow syntactic metric is based on the overlap of base-phrase labels. The semantic...