Abstract not found

Most work in statistical parsing has focused on a single corpus: the Wall Street Journal portion of the Penn Treebank. While this has allowed for quantitative comparison of parsing techniques, it has left open the question of how other types of text might a#ect parser performance, and how portable parsing models are across corpora. We examine these questions by comparing results for the Brown and WSJ corpora, and also consider which parts of the parser's probability model are particularly tuned to the corpus on which it was trained. This leads us to a technique for pruning parameters to reduce the size of the parsing model. 1

ions. (In the near future, the worldwide trend will be for broadcasts to feature closed captions.) Thus we use closed-caption texts as transcripts for news videos. In addition, we employ video-caption detection and recognition. We used "CNN Headline News" as our primary source of news for our experiments. Given image sequences, transcripts, and video captions as information sources, Name-It associates extracted faces with extracted name candidates using the correlation of their timing information and face similarity information. Video captions are also taken into account as supplementary information. To associate faces and names, Name-It integrates several advanced image processing and natural-language processing techniques ---face sequence extraction and similarity evaluation from videos, name extraction from transcripts, and video-caption recognition. Although these technologies aren't always highly accurate, integrating these results will help the system achieve more accurate output

We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for approximate learning and inference. As a parsing algorithm, BP is both asymptotically and empirically efficient. Even with second-order features or latent variables, which would make exact parsing considerably slower or NP-hard, BP needs only O(n3) time with a small constant factor. Furthermore, such features significantly improve parse accuracy over exact first-order methods. Incorporating additional features would increase the runtime additively rather than multiplicatively. 1

This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO’s precision at 95% – as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO’s data.

Two trends are evident iu the recent evolution of the field of information extraction: a preference for simple, often corpus-driven techniques over linguistically sophisticated ones; and a broadening of the central problem definition to include many non-traditional text domains. This development calls for information extraction systems which are as retawetable and general as possible. Here, we describe SRV, a learning archi- tecture for information extraction which is de- signed for maximum generality and flexibility.

In recent work, we described a data prefetch mechanism for pointer-intensive and numeric computations, and presented some aggregate measurements on a suite of benchmarks to quantify its performance potential [MH95]. The basis for this device is a simple classification of memory access patterns in programs that we introduced earlier [HM94]. In this paper we take a close look at two codes from our suite, an English parser called Link-Gram, and the circuit simulation program spice2g6, and present a detailed analysis of them in the context of our model. Focusing on just two programs allows us to display a wider range of data, and discuss relevant code fragments extracted from their source distributions. Results from this study provide a deeper understanding of our memory access classification scheme, and suggest additional optimizations for future data prefetch mechanisms. Keywords: CPU architecture, data cache, memory access pattern classification, instruction profiling, memory latency t...

This research was funded in part through fellowships from the Litton and Intel Corporations. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the author and do not necessarily reflect those of the sponsors.

This chapter introduces weighted bilexical grammars, a formalism in which individual lexical items, such as verbs and their arguments, can have idiosyncratic selectional influences on each other. Such ‘bilexicalism ’ has been a theme of much current work in parsing. The new formalism can be used to describe bilexical approaches to both dependency and phrase-structure grammars, and a slight modification yields link grammars. Its scoring approach is compatible with a wide variety of probability models. The obvious parsing algorithm for bilexical grammars (used by most previous authors) takes time O(n^5). A more efficient O(n³) method is exhibited. The new algorithm has been implemented and used in a large parsing experiment (Eisner, 1996b). We also give a useful extension to the case where the parser must undo a stochastic transduction that has altered the input.

This technical report is an appendix to Eisner (1996): it gives superior experimental results that were reported only in the talk version of that paper, with details of how the results were obtained. Eisner (1996) trained three probability models on a small set of about 4,000 conjunction-free, dependencygrammar parses derived from the Wall Street Journal section of the Penn Treebank, and then evaluated the models on a held-out test set, using a novel O(n 3 ) parsing algorithm. The present paper describes some details of the experiments and repeats them with a larger training set of 25,000 sentences. As reported at the talk, the more extensive training yields greatly improved performance, cutting in half the error rate of Eisner (1996). Nearly half the sentences are parsed with no misattachments; two-thirds of sentences are parsed with at most one misattachment. Of the models described in the original paper, the best score is obtained with the generative \model C," which att...