We describe the evolution of the Entities, Relations and Events (ERE) annotation task, created to support research and technology development within the DARPA DEFT program. We begin by describing the specification for Light ERE annotation, including the motivation for the task within the context of DEFT. We discuss the transition from Light ERE to a more complex Rich ERE specification, enabling more comprehensive treatment of phenomena of interest to DEFT.
The Penn Treebank has recently implemented a new syntactic annotation scheme, designed to highlight aspects of predicate-argument structure. This paper discusses the implementation of crucial aspects of this new annotation scheme. It incorporates a more consistent treatment of a wide range of grammatical phenomena, provides a set of coindexed null elements in what can be thought of as "underlying" position for phenomena such as wh-movement, passive, and the subjects of infinitival constructions, provides some non-context free annotational mechanism to allow the structure of discontinuous constituents to be easily recovered, and allows for a clear, concise tagging system for some semantic roles.
This paper describes the processes and issues of annotating event nuggets based on DEFT ERE Annotation Guidelines v1.3 and TAC KBP Event Detection Annotation Guidelines 1.7. Using Brat Rapid Annotation Tool (brat), newswire and discussion forum documents were annotated. One of the challenges arising from human annotation of documents is annotators' disagreement about the way of tagging events. We propose using Event Nuggets to help meet the definitions of the specific type/subtypes which are part of this project. We present case studies of several examples of event annotation issues, including discontinuous multi-word events representing single events. Annotation statistics and consistency analysis is provided to characterize the interannotator agreement, considering single term events and multi-word events which are both continuous and discontinuous. Consistency analysis is conducted using a scorer to compare first pass annotated files against adjudicated files.
This paper describes the process of creating a novel resource, a parallel Arabizi-Arabic script corpus of SMS/Chat data. The language used in social media expresses many differences from other written genres: its vocabulary is informal with intentional deviations from standard orthography such as repeated letters for emphasis; typos and nonstandard abbreviations are common; and nonlinguistic content is written out, such as laughter, sound representations, and emoticons. This situation is exacerbated in the case of Arabic social media for two reasons. First, Arabic dialects, commonly used in social media, are quite different from Modern Standard Arabic phonologically, morphologically and lexically, and most importantly, they lack standard orthographies. Second, Arabic speakers in social media as well as discussion forums, SMS messaging and online chat often use a non-standard romanization called Arabizi. In the context of natural language processing of social media Arabic, transliterating from Arabizi of various dialects to Arabic script is a necessary step, since many of the existing state-of-the-art resources for Arabic dialect processing expect Arabic script input. The corpus described in this paper is expected to support Arabic NLP by providing this resource.
In this paper we address the following questions from our experience of the last two and a half years in developing a large-scale corpus of Arabic text annotated for morphological information, part-of-speech, English gloss, and syntactic structure: (a) How did we 'leapfrog' through the stumbling blocks of both methodology and training in setting up the Penn Arabic Treebank (ATB) annotation? (b) How did we reconcile the Penn Treebank annotation principles and practices with the Modern Standard Arabic (MSA) traditional and more recent grammatical concepts? (c) What are the current issues and nagging problems? (d) What has been achieved and what are our future expectations?
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