Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to capture all information for name disambiguation in a unified approach, and how to determine the number of people K in the disambiguation process. In this paper, we formalize the problem in a unified probabilistic framework, which incorporates both attributes and relationships. Specifically, we define a disambiguation objective function for the problem and propose a two-step parameter estimation algorithm. We also investigate a dynamic approach for estimating the number of people K. Experiments show that our proposed framework significantly outperforms four baseline methods of using clustering algorithms and two other previous methods. Experiments also indicate that the number K automatically found by our method is close to the actual number.

/pdf/a-unified-probabilistic-framework-for-name-disambiguation-in-4xmfnpqv56.pdf

A Unified Probabilistic Framework for Name Disambiguation in Digital Library

We consider the problem of automatically inferring latent character types in a collection of 15,099 English novels published between 1700 and 1899. Unlike prior work in which character types are assumed responsible for probabilistically generating all text associated with a character, we introduce a model that employs multiple effects to account for the influence of extra-linguistic information (such as author). In an empirical evaluation, we find that this method leads to improved agreement with the preregistered judgments of a literary scholar, complementing the results of alternative models.

/pdf/a-bayesian-mixed-effects-model-of-literary-character-3134hn2sl0.pdf

A Bayesian Mixed Effects Model of Literary Character

We describe a method for identifying the speakers of quoted speech in natural-language textual stories. We have assembled a corpus of more than 3,000 quotations, whose speakers (if any) are manually identified, from a collection of 19th and 20th century literature by six authors. Using rule-based and statistical learning, our method identifies candidate characters, determines their genders, and attributes each quote to the most likely speaker. We divide the quotes into syntactic classes in order to leverage common discourse patterns, which enable rapid attribution for many quotes. We apply learning algorithms to the remainder and achieve an overall accuracy of 83%.

/pdf/automatic-attribution-of-quoted-speech-in-literary-narrative-4x6lpbi00u.pdf

Automatic attribution of quoted speech in literary narrative

This thesis describes new approaches to the formal modeling of narrative discourse. Although narratives of all kinds are ubiquitous in daily life, contemporary text processing techniques typically do not leverage the aspects that separate narrative from expository discourse. We describe two approaches to the problem. The first approach considers the conversational networks to be found in literary fiction as a key aspect of discourse coherence; by isolating and analyzing these networks, we are able to comment on longstanding literary theories. The second approach proposes a new set of discourse relations that are specific to narrative. By focusing on certain key aspects, such as agentive characters, goals, plans, beliefs, and time, these relations represent a theory-of-mind interpretation of a text. We show that these discourse relations are expressive, formal, robust, and through the use of a software system, amenable to corpus collection projects through the use of trained annotators. We have procured and released a collection of over 100 encodings, covering a set of fables as well as longer texts including literary fiction and epic poetry. We are able to inferentially find similarities and analogies between encoded stories based on the proposed relations, and an evaluation of this technique shows that human raters prefer such a measure of similarity to a more traditional one based on the semantic distances between story propositions.

/pdf/modeling-narrative-discourse-1qksvckt2i.pdf

Modeling narrative discourse

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

In this paper, we describe an interactive system, built within the context of CLiMB project, which permits a user to locate the occurrences of named entities within a given text. The named entity tool was developed to identify references to a single art object (e.g. a particular building) with high precision in text related to images of that object in a digital collection. We start with an authoritative list of art objects, and seek to match variants of these named entities in related text. Our approach is to "decay" entities into progressively more general variants while retaining high precision. As variants become more general, and thus more ambiguous, we propose methods to disambiguate intermediate results. Our results will be used to select records into which automatically generated metadata will be loaded.

/pdf/methods-for-precise-named-entity-matching-in-digital-5aruxc23o9.pdf

Methods for precise named entity matching in digital collections

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

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

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Methods for precise named entity matching in digital collections

Extending metadata definitions by automatically extracting and organizing glossary definitions

Building a terminological database from heterogeneous definitional sources

A portal for access to complex distributed information about energy