A Formal Framework for Linguistic Annotation

`Linguistic annotation' covers any descriptive or analytic notations applied to raw language data. The basic data may be in the form of time functions -- audio, video and/or physiological recordings -- or it may be textual. The added notations may include transcriptions of all sorts (from phonetic features to discourse structures), part-of-speech and sense tagging, syntactic analysis, `named entity' identification, co-reference annotation, and so on. While there are several ongoing efforts to provide formats and tools for such annotations and to publish annotated linguistic databases, the lack of widely accepted standards is becoming a critical problem. Proposed standards, to the extent they exist, have focussed on file formats. This paper focuses instead on the logical structure of linguistic annotations. We survey a wide variety of existing annotation formats and demonstrate a common conceptual core, the annotation graph. This provides a formal framework for constructing, maintaining and searching linguistic annotations, while remaining consistent with many alternative data structures and file formats.Comment: 49 page

Modelling Discourse-related terminology in OntoLingAnnot’s ontologies

Recently, computational linguists have shown great interest in discourse annotation in an attempt to capture the internal relations in texts. With this aim, we have formalized the linguistic knowledge associated to discourse into different linguistic ontologies. In this paper, we present the most prominent discourse-related terms and concepts included in the ontologies of the OntoLingAnnot annotation model. They show the different units, values, attributes, relations, layers and strata included in the discourse annotation level of the OntoLingAnnot model, within which these ontologies are included, used and evaluated

Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus

The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning

Natural Language Dialogue Service for Appointment Scheduling Agents

Appointment scheduling is a problem faced daily by many individuals and organizations. Cooperating agent systems have been developed to partially automate this task. In order to extend the circle of participants as far as possible we advocate the use of natural language transmitted by e-mail. We describe COSMA, a fully implemented German language server for existing appointment scheduling agent systems. COSMA can cope with multiple dialogues in parallel, and accounts for differences in dialogue behaviour between human and machine agents. NL coverage of the sublanguage is achieved through both corpus-based grammar development and the use of message extraction techniques.Comment: 8 or 9 pages, LaTeX; uses aclap.sty, epsf.te

Automatic F-Structure Annotation from the AP Treebank

We present a method for automatically annotating treebank resources with functional structures. The method defines systematic patterns of correspondence between partial PS configurations and functional structures. These are applied to PS rules extracted from treebanks. The set of techniques which we have developed constitute a methodology for corpus-guided grammar development. Despite the widespread belief that treebank representations are not very useful in grammar development, we show that systematic patterns of c-structure to f-structure correspondence can be simply and successfully stated over such rules. The method is partial in that it requires manual correction of the annotated grammar rules

Empirical Methodology for Crowdsourcing Ground Truth

The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods for populating the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, in many domains, such as event detection, there is ambiguity in the data, as well as a multitude of perspectives of the information examples. We present an empirically derived methodology for efficiently gathering of ground truth data in a diverse set of use cases covering a variety of domains and annotation tasks. Central to our approach is the use of CrowdTruth metrics that capture inter-annotator disagreement. We show that measuring disagreement is essential for acquiring a high quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical Relation Extraction, Twitter Event Identification, News Event Extraction and Sound Interpretation. We also show that an increased number of crowd workers leads to growth and stabilization in the quality of annotations, going against the usual practice of employing a small number of annotators.Comment: in publication at the Semantic Web Journa

Multi-Tier Annotations in the Verbmobil Corpus

In very large and diverse scientific projects where as different groups as linguists and engineers with different intentions work on the same signal data or its orthographic transcript and annotate new valuable information, it will not be easy to build a homogeneous corpus. We will describe how this can be achieved, considering the fact that some of these annotations have not been updated properly, or are based on erroneous or deliberately changed versions of the basis transcription. We used an algorithm similar to dynamic programming to detect differences between the transcription on which the annotation depends and the reference transcription for the whole corpus. These differences are automatically mapped on a set of repair operations for the transcriptions such as splitting compound words and merging neighbouring words. On the basis of these operations the correction process in the annotation is carried out. It always depends on the type of the annotation as well as on the position and the nature of the difference, whether a correction can be carried out automatically or has to be fixed manually. Finally we present a investigation in which we exploit the multi-tier annotations of the Verbmobil corpus to find out how breathing is correlated with prosodic-syntactic boundaries and dialog acts. 1

Non-hierarchical Structures: How to Model and Index Overlaps?

Overlap is a common phenomenon seen when structural components of a digital object are neither disjoint nor nested inside each other. Overlapping components resist reduction to a structural hierarchy, and tree-based indexing and query processing techniques cannot be used for them. Our solution to this data modeling problem is TGSA (Tree-like Graph for Structural Annotations), a novel extension of the XML data model for non-hierarchical structures. We introduce an algorithm for constructing TGSA from annotated documents; the algorithm can efficiently process non-hierarchical structures and is associated with formal proofs, ensuring that transformation of the document to the data model is valid. To enable high performance query analysis in large data repositories, we further introduce an extension of XML pre-post indexing for non-hierarchical structures, which can process both reachability and overlapping relationships.Comment: The paper has been accepted at the Balisage 2014 conferenc