7534908
Building a hypertextual digital library in the humanities: a case study on London
This paper describes the creation of a new humanities digital library collection: 11,000,000 words and 10,000 images representing books, images and maps on pre-twentieth century London and its environs. The London collection contained far more dense and precise information than the materials from the Greco-Roman world on which we had previously concentrated. The London collection thus allowed us to explore new problems of data structure, manipulation, and visualization. This paper contrasts our model for how humanities digital libraries are best used with the assumptions that underlie many academic digital libraries on the one hand and more literary hypertexts on the other. Since encoding guidelines such as those from the TEI provide collection designers with far more options than any one project can realize, this paper describes what structures we used to organize the collection and why. We particularly emphasize the importance of mining historical authority lists (encyclopedias, gazetteers, etc.) and then generating automatic span-to-span links within the collection.
1432381
Detecting and Browsing Events in Unstructured text
Previews and overviews of large, heterogeneous information resources help users comprehend the scope of collections and focus on particular subsets of interest. For narrative documents, questions of "what happened? where? and when?" are natural points of entry. Building on our earlier work at the Perseus Project with detecting terms, place names, and dates, we have exploited co-occurrences of dates and place names to detect and describe likely events in document collections. We compare statistical measures for determining the relative significance of various events. We have built interfaces that help users preview likely regions of interest for a given range of space and time by plotting the distribution and relevance of various collocations. Users can also control the amount of collocation information in each view. Once particular collocations are selected, the system can identify key phrases associated with each possible event to organize browsing of the documents themselves.
13000609
Disambiguating Geographic Names in a Historical Digital Library
Geographic interfaces provide natural, scalable visualizationss for many digital library collections, but the wide range of data in digital libraries presents some particular problems for identifying and disambiguating place names. We describe the toponym-disambiguation system in the Perseus digital library and evaluate its performance. Name categorization varies significantly among different types of documents, but toponym disambiguation performs at a high level of precision and recall with a gazetteer an order of magnitude larger than most other applications.
2813562
Context-Based Morphological Disambiguation with Random Fields
Finite-state approaches have been highly successful at describing the morphological processes of many languages. Such approaches have largely focused on modeling the phone- or character-level processes that generate candidate lexical types, rather than tokens in context. For the full analysis of words in context, disambiguation is also required (Hakkani-Tur et al., 2000; Hajic et al., 2001). In this paper, we apply a novel source-channel model to the problem of morphological disambiguation (segmentation into morphemes, lemmatization, and POS tagging) for concatenative, templatic, and inflectional languages. The channel model exploits an existing morphological dictionary, constraining each word's analysis to be linguistically valid. The source model is a factored, conditionally-estimated random field (Lafferty et al., 2001) that learns to disambiguate the full sentence by modeling local contexts. Compared with baseline state-of-the-art methods, our method achieves statistically significant error rate reductions on Korean, Arabic, and Czech, for various training set sizes and accuracy measures.
2029665
Minimum Risk Annealing for Training Log-Linear Models
When training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set. Since the error surface for many natural language problems is piecewise constant and riddled with local minima, many systems instead optimize log-likelihood, which is conveniently differentiable and convex. We propose training instead to minimize the expected loss, or risk. We define this expectation using a probability distribution over hypotheses that we gradually sharpen (anneal) to focus on the 1-best hypothesis. Besides the linear loss functions used in previous work, we also describe techniques for optimizing nonlinear functions such as precision or the BLEU metric. We present experiments training log-linear combinations of models for dependency parsing and for machine translation. In machine translation, annealed minimum risk training achieves significant improvements in BLEU over standard minimum error training. We also show improvements in labeled dependency parsing.
11953962
Detecting events with date and place information in unstructured text
Digital libraries of historical documents provide a wealth of information about past events, often in unstructured form. Once dates and place names are identified and disambiguated, using methods that can differ by genre, we examine collocations to detect events. Collocations can be ranked by several measures, which vary in effectiveness according to type of events, but the log-likelihood measure (-2 log λ) offers a reasonable balance between frequently and infrequently mentioned events and between larger and smaller spatial and temporal ranges. Significant date-place collocations can be displayed on timelines and maps as an interface to digital libraries. More detailed displays can highlight key names and phrases associated with a given event.
7140389
Overcoming Ontology Mismatches in Transactions with Self-Describing Service Agents
One vision of the "Semantic Web" of the future is that software agents will interact with each other using formal metadata that reveal their interfaces. We examine one plausible paradigm, where agents provide service descriptions that tell how they can be used to accomplish other agents' goals. From the point of view of these other agents, the problem of deciphering a service description is quite similar to the standard AI planning problem, with some interesting twists. Two such twists are the possibility of having to reconcile contradictory ontologies -- or conceptual frameworks -- used by the agent, and having to rearrange the data structures of a message-sending agent so they match the expectations of the recipient. We argue that the former problem requires human intervention and maintenance, but that the latter can be fully automated.
8267445
Explaining design plans
The paper describes a methodology for generating quality explanations from design plans. In selecting items of information for inclusion in an explanation, the methodology considers the rationales behind the strategic knowledge and object-level knowledge specified in a design plan. Rhetorical structure theory and discourse focus tracking are utilised to determine textual structure, and to maintain discourse coherence and cohesion. Using a simple system of user modelling, it is shown how the methodology flexibly includes consideration of a user's knowledge, thus resulting in more focused and convincing explanations. The methodology is used to investigate follow-up explanations, and the generation of explanations from plans which contain a misconception on the part of the designer.
8670255
Applications of a Strategy for Designing Divide-and-Conquer Algorithms
Abstract A strategy for designing divide-and-conquer algorithms that was originally presented in a previous article is extended and applied to several new problems. The extension involves techniques for modifying the original specification based on specific kinds of failures that can occur during the design process. We derive several divide-and-conquer algorithms that are substantially more efficient than previously known algorithms. This paper also emphasizes the naturalness with which divide-and-conquer algorithms can be transformed into a parallel format. One problem explored is to find the maximum sum over all rectangular subregions of a given matrix of integers. For an n × n matrix there is a straightforward O(n6) enumeration algorithm. We derive a O(n3) divide-and-conquer algorithm, then show that it can be executed in O(log2n) time in parallel and, furthermore, with pipelining of inputs it can be executed with O(1) time between successive outputs.
44972038
Project CALC: Calculus as a Laboratory Course
Calculus is the study of change. The concepts of calculus enable us to model processes that change and to describe properties of these processes that remain constant in the midst of change. Now change has come to the learning of calculus -change driven by the need to respond to the revolution in technology and fueled by funds from the United States National Science Foundation. What should be changed? How and how fast? What should remain constant? In this paper we describe one answer to these questions: Project CALC, a new calculus course developed at Duke University. The key features of our approach are real-world problems, hands-on activities, discovery learning, writing and revision of writing, teamwork, and intelligent use of available tools.
12681881
Synthesis of Constraint Algorithms