Oleksandr Kolomiyets

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Many NLP applications require information about locations of objects referenced in text, or relations between them in space. For example , the phrase a book on the desk contains information about the location of the object book, as trajector, with respect to another object desk, as landmark. Spatial Role Labeling (SpRL) is an evaluation task in the(More)
We propose a new approach to characterizing the timeline of a text: temporal dependency structures, where all the events of a narrative are linked via partial ordering relations like BEFORE , AFTER, OVERLAP and IDENTITY. We annotate a corpus of children's stories with temporal dependency trees, achieving agreement (Krippendorff's Alpha) of 0.856 on the(More)
We explore a semi-supervised approach for improving the portability of time expression recognition to non-newswire domains: we generate additional training examples by substituting temporal expression words with potential synonyms. We explore using synonyms both from WordNet and from the Latent Words Language Model (LWLM), which predicts synonyms in context(More)
In this paper we describe a system for the recognition and normalization of temporal expressions (Task 13: TempEval-2, Task A). The recognition task is approached as a classification problem of sentence constituents and the normalization is implemented in a rule-based manner. One of the system features is extending positive annotations in the corpus by(More)
We present an approach to annotating timelines in stories where events are linked together by temporal relations into a temporal dependency tree. This approach avoids the disconnected timeline problems of prior work, and results in timelines that are more suitable for temporal reasoning. We show that annotating timelines as temporal dependency trees is(More)
In this paper we describe the technical implementation of our system that participated in the Helping Our Own 2012 Shared Task (HOO-2012). The system employs a number of preprocessing steps and machine learning classifiers for correction of determiner and preposition errors in non-native English texts. We use maximum entropy classifiers trained on the(More)
Temporal expressions are important structures in natural language. In order to understand text, temporal expressions have to be extracted and normalized. In this paper we present and compare two approaches for the automatic recognition of temporal expressions, based on a supervised machine learning approach and trained on TimeBank. The first approach(More)
This paper describes a system for temporal processing of text, which participated in the Temporal Evaluations 2013 campaign. The system employs a number of machine learning classifiers to perform the core tasks of: identification of time expressions and events, recognition of their attributes, and estimation of temporal links between recognized events and(More)