Fatemeh Torabi Asr

Conceptual and Practical Steps in Event Coreference Analysis of Large-scale Data

A simple conceptual model is employed to investigate events, and break the task of coreference resolution into two steps: semantic class detection and similaritybased matching. With this perspective an algorithm is implemented to cluster event mentions in a large-scale corpus. Results on test data from AQUAINT TimeML, which we annotated manually with coreference links, reveal how semantic conventions vs. information available in the context of event mentions affect decisions in coreference analysis.

An Artificial Language Evaluation of Distributional Semantic Models

Recent studies of distributional semantic models have set up a competition between word embeddings obtained from predictive neural networks and word vectors obtained from abstractive count-based models. This paper is an attempt to reveal the underlying contribution of additional training data and post-processing steps on each type of model in word similarity and relatedness inference tasks. We do so by designing an artificial language framework, training a predictive and a count-based model on data sampled from this grammar, and evaluating the resulting word vectors in paradigmatic and syntagmatic tasks defined with respect to the grammar.