This article is a survey of methods for measuring agreement among corpus annotators. It exposes the mathematics and underlying assumptions of agreement coefficients, covering Krippendorff's alpha as well as Scott's pi and Cohen's kappa; discusses the use of coefficients in several annotation tasks; and argues that weighted, alpha-like coefficients, traditionally less used than kappa-like measures in computational linguistics, may be more appropriate for many corpus annotation tasks---but that their use makes the interpretation of the value of the coefficient even harder.

/pdf/inter-coder-agreement-for-computational-linguistics-2mpuyn1mc8.pdf

Inter-coder agreement for computational linguistics

We explore probabilistic lexico-syntactic pattern matching, also known as soft pattern matching, in a definitional question answering system. Most current systems use regular expression-based hard matching patterns to identify definition sentences. Such rigid surface matching often fares poorly when faced with language variations. We propose two soft matching models to address this problem: one based on bigrams and the other on the Profile Hidden Markov Model (PHMM). Both models provide a theoretically sound method to model pattern matching as a probabilistic process that generates token sequences. We demonstrate the effectiveness of the models on definition sentence retrieval for definitional question answering. We show that both models significantly outperform the state-of-the-art manually constructed hard matching patterns on recent TREC data.A critical difference between the two models is that the PHMM has a more complex topology. We experimentally show that the PHMM can handle language variations more effectively but requires more training data to converge.While we evaluate soft pattern models only on definitional question answering, we believe that both models are generic and can be extended to other areas where lexico-syntactic pattern matching can be applied.

Soft pattern matching models for definitional question answering

We present the Omega ontology, a large terminological ontology obtained by remerging WordNet and Mikrokosmos, adding information from various other sources, and subordinating the result to a newly designed feature-oriented upper model. We explain the organizing principles of the representation used for Omega and discuss the methodology used to merge the constituent conceptual hierarchies. We survey a range of auxiliary knowledge sources (including instances, verb frame annotations, and domainspecific sub-ontologies) incorporated into the basic conceptual structure and applications that have benefited from Omega. Omega is available for browsing at http://omega.isi.edu/.

The Omega Ontology

This paper presents and analyzes an annotated corpus of definitions, created to train an algorithm for the automatic extraction of definitions and hypernyms from web documents. As an additional resource, we also include a corpus of non-definitions with syntactic patterns similar to those of definition sentences, e.g.: ""An android is a robot"" vs. ""Snowcap is unmistakable"". Domain and style independence is obtained thanks to the annotation of a large and domain-balanced corpus and to a novel pattern generalization algorithm based on word-class lattices (WCL). A lattice is a directed acyclic graph (DAG), a subclass of nondeterministic finite state automata (NFA). The lattice structure has the purpose of preserving the salient differences among distinct sequences, while eliminating redundant information. The WCL algorithm will be integrated into an improved version of the GlossExtractor Web application (Velardi et al., 2008). This paper is mostly concerned with a description of the corpus, the annotation strategy, and a linguistic analysis of the data. A summary of the WCL algorithm is also provided for the sake of completeness.

/pdf/an-annotated-dataset-for-extracting-definitions-and-3it4l8rioc.pdf

An Annotated Dataset for Extracting Definitions and Hypernyms from the Web

[서평]「Electric Words : Dictionaries, Computers and Meanings」

Metadata descriptions of database contents are required to build and use systems that access and deliver data in response to user requests. When numerous heterogeneous databases are brought together in a single system, their various metadata formalizations must be homogenized and integrated in order to support the access planning and delivery system. This integration is a tedious process that requires human expertise and attention. In this paper we describe a method of speeding up the formalization and integration of new metadata. The method takes advantage of the fact that databases are often described in web pages containing natural language glossaries that define pertinent aspects of the data. Given a root URL, our method identifies likely glossaries, extracts and formalizes aspects of relevant concepts defined in them, and automatically integrates the new formalized metadata concepts into a large model of the domain and associated conceptualizations. This demo will show the end-to-end performance of this system.The demonstration will show the concept acquisition and placement process. Using the AskCal interface (Philpot et al, 2002), we will ask the viewer to interact with the system to retrieve some data. We will then introduce a new topic, one for whom there is no concept yet in the ontology. We will browse the ontology to verify this. We will identify candidate glossary web pages or sites using prior work (Klavans et al, 2002) and/or a web-accessible term finder. In a separate window, we will then display a glossary-containing file from www.eia.gov or similar, in which the concept is defined in text. Using the system described in the accompanying paper, we will enter the root URL and activate the glossary analysis and concept alignment procedures. Upon conclusion, the system will announce the acquisition and placement of as many concepts as it has found. We will then re-display the ontology in the browser. Newly acquired concepts will be displayed in a different color. The user will be free to click on the concepts in order to examine the formalized contents, as well as hyperclick back to the glossary source page for verification. This demo will thus illustrate not only our latest work, but the major part of the EDC system that we have been building over the past 4 years.

Extending metadata definitions by automatically extracting and organizing glossary definitions

This paper addresses the problem of developing methods to be used in the identification and extraction of meaningful semantic components from large online glossaries. We present two sets of results. First, we report on the algorithm, ParseGloss, which was used to analyze definitions, and extract the main concept, or genus phrase. We ran the system on over 12,000 online glossary entries. Second, we present a method to evaluate our results, using human judgments on a collection of definitions from six different sources. This paper discusses our approach to the evaluation process, since the creation of a standard for evaluation is in itself a contribution to the field. The methods we have developed have required addressing the significant challenges of abstracting a single gold standard from multiple naive, human judgments on a highly subjective task. Once the method for creating the standard was developed, we then established the gold standard data. We report on our performance in running ParseGloss over this controlled collection of definitions. Our first set of results presents precision and recall on system performance. Our second results are presented in terms of techniques for determining agreement between human subjects. Success in the ParseGloss algorithm will contribute to the automatic creation of ontologies.

/pdf/tackling-the-internet-glossary-glut-automatic-extraction-and-28j1jwd59x.pdf

Tackling the Internet Glossary Glut: Automatic Extraction and Evaluation of Genus Phrases

An obstacle to understanding results across heterogeneous databases is the ability to determine conceptual connections between differing terminologies. In this paper, we present the two step approach which we have used to build a terminological database in order to address this issue. First we automatically built a heterogeneous collection of terms and definitions from two types of dynamic sources: 1) glossaries automatically identified from 147 government web sites and 2) definitions extracted from 600 unstructured articles. After storing terms and their definitions, we semantically analyzed the definitions to store the terminological knowledge in a relational database. Currently the database contains 12,780 definitions of 8,431 terms.

Building a terminological database from heterogeneous definitional sources

The Digital Government Research Center (DGRC) has completed phase one of the Energy Data Collection (EDC) project. In this paper, we present the results of building and evaluating system components, along with plans for phase two of the project. Phase one focused on data about petroleum products' prices and volumes, provided by the Energy Information Administration, the Bureau of Labor Statistics, and the Census Bureau, and the California Energy Commission, in the form of over 50,000 data tables. This research centers on providing dynamically planned access to multiple non-homogeneous databases and other data collections, using a query planner and a largescale (90,000-node) concept ontology and a domain model, both of which are accessed via various interfaces, including cascaded menus, a natural language question analyzer, and an ontology browser. Other data access research focuses on the caching and very fast display of massive amounts of data. In order to more rapidly construct the domain models, systems were developed for automatically identifying terminology glossary files from websites, extracting and formalizing the glossary definitions, clustering them appropriately, and automatically embedding them into the existing ontology and domain model. Work on evaluation focuses on measuring the effectiveness of the use of this system by a variety of users at various levels of expertise.

/pdf/a-portal-for-access-to-complex-distributed-information-about-360odfvo0l.pdf

A portal for access to complex distributed information about energy

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Extending metadata definitions by automatically extracting and organizing glossary definitions

Tackling the Internet Glossary Glut: Automatic Extraction and Evaluation of Genus Phrases

Building a terminological database from heterogeneous definitional sources

A portal for access to complex distributed information about energy