11578326
Drudgery and deep thought
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Digital libraries of historical documents provide a wealth of information about past events, often in unstructured form.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.
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.
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.
30879884
Generation of lower bounds for minimum span frequency assignment
Minimum span frequency assignment problems require lower bounds for the span in order to assess the quality of individual assignments, and to effectively compare different meta-heuristic algorithms. The generation of good lower bounds requires the identification of critical subproblems. These subproblems are subgraphs of a constraint graph. Sometimes clique subgraphs lead to good lower bounds. However for some problems the clique must be modified in order to obtain the best possible bound. This can be time consuming, requires manual intervention and is dependent on the initial clique obtained and the specific problem. For practical use, a simple bounding technique is needed that can be universally applied to all problems without modification. Two algorithms based on meta-heuristics are presented that aim to find a subproblem that gives the best possible lower bound. This avoids the need for clique detection routines and manual intervention, and gives a robust and automatic method for generating lower bounds for all minimum span problems. These algorithms use mathematical programming formulations of the frequency assignment problem. Extensive testing on a wide range of problems shows that bounds from the new algorithms match those using previous techniques, and in some cases are significantly better.
14595517
Reasoning by Cases and the Formation of Conditional Programs
Reasoning by cases, a natural feature of human reasoning, has been difficult to formulate so that it can be performed naturally when needed. Several difficulties arise: (1) how to motivate the use of reasoning by cases when and only when needed, (2) how to determine an appropriate analysis of the goal into cases, and (3) how to carry out the deduction in each case and combine the results. In this paper we focus on how reasoning by cases can be naturally accomplished in the framework of derived antecedents (Smith 1982). Our main technical contributions are (1) a set of strategies that draw on the context of a deduction to provide an appropriate case analysis for a goal, and (2) inference rules for carrying out reasoning by cases and forming conditional terms for the existentially quantified variables in the initial goal.
12681881
Synthesis of Constraint Algorithms
1180538
mSpace mobile: a UI gestalt to support on-the-go info-interaction
mSpace Mobile Interaction presents a UI gestalt of 7 techniques for mobile/on-the-move information retrieval and assessment that enables multiple views of the information within a persistent focus+context viewer. It uses the web but breaks the web page paradigm to support effective rapid triage.
16901228
Planware-domain-specific synthesis of high-performance schedulers
Planware is a domain-specific generator of high-performance scheduling software, currently being developed at the Kestrel Institute. Architecturally, Planware is an extension of the Specware system with domain-independent and domain-dependent parts. The domain-independent part includes a general algorithm design facility (including mechanisms to synthesize global-search and constraint propagation algorithms), as well as support for theorem-proving and witness finding. The domain-dependent part includes scheduling domain knowledge and architecture representations, and other domain-specific refinement knowledge that relates the scheduling domain to general algorithm design and data type refinement. Using Planware, the user interactively specifies a problem and then the system automatically generates a formal specification and refines it.