Articles

We investigate single-view algorithms as an al- ternative to multi-view algorithms for weakly supervised learning for natural language pro- cessing tasks without a natural feature split. In particular, we apply co-training, self-training, and EM to one such task and find that both self- training and FS-EM, a new variation of …...

Recent TREC results have demonstrated the need for deeper text understanding methods. This paper introduces the idea of automated reasoning applied to question answering and shows the feasibility of integrating a logic prover into a Question Answering system. The approach is to transform questions and answer passages into logic representations. …...

We present a derivation of the alignment tem- plate model for statistical machine translation and an implementation of the model using weighted finite state transducers. The approach we describe allows us to implement each con- stituent distribution of the model as a weighted finite state transducer or acceptor. We show …...

In this paper, we introduce a generative prob- abilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transforma- tion into the noisy output of an OCR system. The model is designed for use in error …...

We present an extension of the classic A* search procedure to tabular PCFG parsing. The use of A* search can dramatically reduce the time required to find a best parse by conservatively estimating the probabilities of parse completions. We discuss vari- ous estimates and give efficient algorithms for com- puting …...

We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previ- ously proposed phrase-based translation mod- els. Within our framework, we carry out a large number of experiments to understand bet- ter and explain why phrase-based models out- perform word-based models. Our …...

Manually constructing an inventory of word senses has suffered from problems including high cost, arbitrary assignment of meaning to words, and mismatch to domains. To over- come these problems, we propose a method to assign word meaning from a bilingual comparable corpus and a bilingual dictionary. It clusters second-language translation …...

Following the recent adoption by the machine translation community of automatic evalua- tion using the BLEU/NIST scoring process, we conduct an in-depth study of a similar idea for evaluating summaries. The results show that automatic evaluation using unigram co- occurrences between summary pairs correlates surprising well with human evaluations, based …...

We present a neural network method for induc- ing representations of parse histories and us- ing these history representations to estimate the probabilities needed by a statistical left-corner parser. The resulting statistical parser achieves performance (89.1% F-measure) on the Penn Treebank which is only 0.6% below the best current parser …...

We describe our approach to the construction and evaluation of a large-scale database called “CatVar” which contains categorial variations of English lexemes. Due to the prevalence of cross-language categorial variation in multilin- gual applications, our categorial-variation re- source may serve as an integral part of a di- verse range of …...

In this paper we present ONTOSCORE, a sys- tem for scoring sets of concepts on the basis of an ontology. We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of their semantic coherence. We conducted an annotation exper- iment and showed that human …...

The discovery of semantic relations from text becomes increasingly important for applica- tions such as Question Answering, Informa- tion Extraction, Text Summarization, Text Un- derstanding, and others. The semantic rela- tions are detected by checking selectional con- straints. This paper presents a method and its results for learning semantic constraints …...

We present improvements to a greedy decod- ing algorithm for statistical machine translation that reduce its time complexity from at least cubic ( ����� when applied na¨ıvely) to prac- tically linear time1 without sacrificing trans- lation quality. We achieve this by integrat- ing hypothesis evaluation into hypothesis cre- ation, tiling …...

Previous work on minimizing weighted finite-state automata (including transducers) is limited to particular types of weights. We present efficient new minimization algorithms that apply much more generally, while being simpler and about as fast. We also point out theoretical limits on minimization algo- rithms. We characterize the kind of “well-behaved” …...

This paper describes a novel multi-stage recog- nition procedure for deducing the spelling and pronunciation of an open set of names. The overall goal is the automatic acquisition of un- known words in a human computer conver- sational system. The names are spoken and spelled in a single utterance, achieving …...

We address the text-to-text generation prob- lem of sentence-level paraphrasing — a phe- nomenon distinct from and more difficult than word- or phrase-level paraphrasing. Our ap- proach applies multiple-sequence alignment to sentences gathered from unannotated compara- ble corpora: it learns a set of paraphrasing pat- terns represented by word lattice …...

Motivated by the success of ensemble methods in machine learning and other areas of natu- ral language processing, we developed a multi- strategy and multi-source approach to question answering which is based on combining the re- sults from different answering agents searching for answers in multiple corpora. The answer- ing …...

Named Entity (NE) extraction is an important subtask of document processing such as in- formation extraction and question answering. A typical method used for NE extraction of Japanese texts is a cascade of morphological analysis, POS tagging and chunking. However, there are some cases where segmentation gran- ularity contradicts the …...

This paper describes a method for utterance classification that does not require manual transcription of training data. The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conven- tional word-trigram recognition requiring man- ual transcription. …...

Phrase level translation models are ef- fective in improving translation qual- ity by addressing the problem of local re-ordering across language boundaries. Methods that attempt to fundamentally modify the traditional IBM translation model to incorporate phrases typically do so at a prohibitive computational cost. We present a technique that begins …...