In this paper, we present our exploration of annotating Chinese word senses using English WordNet synsets, with examples extracted from OntoNotes Chinese sense inventories. Given a target word along with the example that contains it, the annotators select a WordNet synset that best describes the meaning of the target word in the context. The result demonstrates an inter-annotator agreement of 38% between two annotators. We delve into the instances of disagreement by comparing the two annotated synsets, including their positions within the WordNet hierarchy. The examination reveals intriguing patterns among closely related synsets, shedding light on similar concepts represented within the WordNet structure. The data offers as an indirect linking of Chinese word senses defined in OntoNotes Chinese sense inventories to WordNet sysnets, and thus promotes the value of the OntoNotes corpus. Compared to a direct linking of Chinese word senses to WordNet synsets, the example-based annotation has the merit of not being affected by inaccurate sense definitions and thus offers a new way of mapping WordNets of different languages. At the same time, the annotated data also serves as a valuable linguistic resource for exploring potential lexical differences between English and Chinese, with potential contributions to the broader understanding of cross-linguistic semantic mapping

This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology.By modeling morphological processes including suffixation, prefixation, infixation, and full and partial reduplication with constrained stem change rules, our system effectively constrains the search space and offers a wide coverage in terms of morphological typology.The system is tested on nine typologically and genetically diverse languages, and shows superior performance over leading systems.We also investigate the effect of an oracle that provides only a handful of bits per language to signal morphological type.

This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program. The data consists of approximately 2000 tokens annotated for morphological segmentation in each of 9 low resource languages, along with root information for 7 of the languages. The languages annotated show a broad diversity of typological features. A minimal annotation scheme for segmentation was developed such that it could capture the patterns of a wide range of languages and also be performed reliably by non-linguist annotators. The basic annotation guidelines were designed to be language-independent, but included language-specific morphological paradigms and other specifications. The resulting annotated corpus is designed to support and stimulate the development of unsupervised morphological segmenters and analyzers by providing a gold standard for their evaluation on a more typologically diverse set of languages than has previously been available. By providing root annotation, this corpus is also a step toward supporting research in identifying richer morphological structures than simple morpheme boundaries.

This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e.g., suffixes) that can be applied to a homogeneous set of words (e.g., nouns or verbs). Our algorithm identifies statistically reliable paradigms from the morphological segmentation result of a probabilistic model, and chooses reliable suffixes from them. The new suffixes can be fed back iteratively to improve the accuracy of the probabilistic model. Finally, the unreliable paradigms are subjected to pruning to eliminate unreliable morphological relations between words. The paradigm-based algorithm significantly improves segmentation accuracy. Our method achieves start-of-the-art results on experiments using the Morpho-Challenge data, including English, Turkish, and Finnish.

We present a novel framework of system combination for multi-document summarization.For each input set (input), we generate candidate summaries by combining whole sentences from the summaries generated by different systems.We show that the oracle among these candidates is much better than the summaries that we have combined.We then present a supervised model to select among the candidates.The model relies on a rich set of features that capture content importance from different perspectives.Our model performs better than the systems that we combined based on manual and automatic evaluations.We also achieve very competitive performance on six DUC/TAC datasets, comparable to the state-of-the-art on most datasets.

Dependency parsing algorithms capable of producing the types of crossing dependencies seen in natural language sentences have traditionally been orders of magnitude slower than algorithms for projective trees. For 95.8–99.8% of dependency parses in various natural language treebanks, whenever an edge is crossed, the edges that cross it all have a common vertex. The optimal dependency tree that satisfies this 1-Endpoint-Crossing property can be found with an O( n4) parsing algorithm that recursively combines forests over intervals with one exterior point. 1-Endpoint-Crossing trees also have natural connections to linguistics and another class of graphs that has been studied in NLP.

This video shows a demonstration of a fully autonomous robot, an iRobot ATRV-JR, which can be given commands using natural language. Users type commands to the robot on a tablet computer, which are then parsed and processed using semantic analysis. This information is used to build a plan representing the high level autonomous behaviors the robot should perform [2][1]. The robot can be given commands to be executed immediately (e.g., "Search the floor for hostages.") as well as standing orders for use over the entire run (e.g., "Let me know if you see any bombs.").

Human-computer interaction has been proposed as a means for more effectively dealing with the challenges posed by information retrieval. However, within the hierarchy of information needs, certain kinds of needs are only poorly handled by existing techniques. Information needs requiring thoroughness (exhaustive research, information discovery) are not well served by existing models of human-computer information retrieval (HCIR). Information needs such as these are commonplace in legal, patent, medical and intelligence searches. In such applications, high recall with high precision is the primary consideration, and the standard model, which treats human interaction as a kind of post-processing filter, does not yield a system with desired characteristics. In this paper, we propose an alternative model of HCIR which yields systems whose properties are more closely aligned with the needs of the exhaustive research task, and describe an implementation of such a system, demonstrating its effectiveness over the standard model in a task that models real-world conditions.

This paper presents a computational model of verb acquisition which uses what we will call the principle of structured overcommitment to eliminate the need for negative evidence. The learner escapes from the need to be told that certain possibilities cannot occur (i.e., are "ungrammatical") by one simple expedient: It assumes that all properties it has observed are either obligatory or forbidden until it sees otherwise, at which point it decides that what it thought was either obligatory or forbidden is merely optional. This model is built upon a classification of verbs based upon a simple three-valued set of features which represents key aspects of a verb's syntactic structure, its predicate/argument structure, and the mapping between them.

Linguists, including computational linguists, have always been fond of talking about trees. In this paper, we outline a theory of linguistic structure which talks about talking about trees; we call this theory Description theory (D-theory). While important issues must be resolved before a complete picture of D-theory emerges (and also before we can build programs which utilize it), we believe that this theory will ultimately provide a framework for explaining the syntax and semantics of natural language in a manner which is intrinsically computational. This paper will focus primarily on one set of motivations for this theory, those engendered by attempts to handle certain syntactic phenomena within the framework of deterministic parsing.

No abstract available.

Two different definitions for the operating characteristics of the S-R-T flip-flop exist in the literature. These two definitions are discussed, and it is argued that one definition is unnecessarily restrictive while the second is not only more logically correct but leads to more economical circuitry.

A simple general method for analyzing any given cascaded binary counter feedback configuration is presented. The count can be determined directly from the diagram without the need of writing equations of any form. The synthesis of cascaded binary counter feedback configurations is also discussed, and a table of feedback configurations with the minimum number of feedback connections is presented.

A transfer tree is a particular type of multiterminal network having a single input which may be connected to any one of a number of outputs. An n-relay transfer tree is partially developed if it has less than the 2n possible output terminals. Rearrangement of a partially-developed tree can lead to a reduction in the total number of transfers required. This paper presents a method of obtaining a required partially-developed transfer tree with the minimum number of transfers.