Articles

This paper proposes the utilization of lexical cohesion to facilitate evaluation of machine translation at the document level. As a linguis- tic means to achieve text coherence, lexical cohesion ties sentences together into a mean- ingfully interwoven structure through words with the same or related meaning. A compar- ison between …...

Many natural language processing problems involve constructing large nearest-neighbor graphs. We propose a system called FLAG to construct such graphs approximately from large data sets. To handle the large amount of data, our algorithm maintains approximate counts based on sketching algorithms. To find the approximate nearest neighbors, our algorithm pairs …...

Constituency parser performance is primarily interpreted through a single metric, F-score on WSJ section 23, that conveys no linguis- tic information regarding the remaining errors. We classify errors within a set of linguisti- cally meaningful types using tree transforma- tions that repair groups of errors together. We use this analysis …...

We propose the subtree ranking approach to parse forest reranking which is a general- ization of current perceptron-based reranking methods. For the training of the reranker, we extract competing local subtrees, hence the training instances (candidate subtree sets) are very similar to those used during beam- search parsing. eter optimization.This …...

Discovering significant types of relations from the web is challenging because of its open nature. Unsupervised algorithms are developed to extract relations from a cor- pus without knowing the relations in ad- vance, but most of them rely on tagging arguments of predefined types. Recently, a new algorithm was proposed …...

We investigate two aspects of the empirical behavior of paired significance tests for NLP systems. First, when one system appears to outperform another, how does significance level relate in practice to the magnitude of the gain, to the size of the test set, to the similar- ity of the systems, …...

We study how to extend a large knowledge base (Freebase) by reading relational informa- tion from a large Web text corpus. Previous studies on extracting relational knowledge from text show the potential of syntactic patterns for extraction, but they do not exploit background knowledge of other relations in the knowledge …...

Current Chinese event extraction systems suffer much from two problems in trigger identification: unknown triggers and word segmentation errors to known triggers. To resolve these problems, this paper proposes two novel inference mechanisms to explore special characteristics in Chinese via compositional semantics inside Chinese triggers and discourse consistency between Chinese …...

Accurate and robust metrics for automatic eval- uation are key to the development of statistical machine translation (MT) systems. We first introduce a new regression model that uses a probabilistic finite state machine (pFSM) to compute weighted edit distance as predictions of translation quality. We also propose a novel pushdown …...

When trained on very large parallel corpora, the phrase table component of a machine translation system grows to consume vast computational resources. In this paper, we in- troduce a novel pruning criterion that places phrase table pruning on a sound theoretical foundation. Systematic experiments on four language pairs under various …...

Phrase-based machine translation models have shown to yield better translations than Word-based models, since phrase pairs en- code the contextual information that is needed for a more accurate translation. However, many phrase pairs do not encode any rele- vant context, which means that the transla- tion event encoded in that …...

We apply two new automated semantic eval- uations to three distinct latent topic models. Both metrics have been shown to align with human evaluations and provide a balance be- tween internal measures of information gain and comparisons to human ratings of coher- ent topics. We improve upon the measures by …...

We propose a technique to generate non- projective word orders in an efficient statisti- cal linearization system. Our approach pre- dicts liftings of edges in an unordered syntac- tic tree by means of a classifier, and uses a projective algorithm for tree linearization. We obtain statistically significant improvements on six …...

We investigate paradigmatic representations of word context in the domain of unsupervised syntactic category acquisition. Paradigmatic representations of word context are based on potential substitutes of a word in contrast to syntagmatic representations based on prop- erties of neighboring words. We compare a bigram based baseline model with several paradigmatic …...

Previous work on paraphrase extraction us- ing parallel or comparable corpora has gener- ally not considered the documents’ discourse structure as a useful information source. We propose a novel method for collecting para- phrases relying on the sequential event or- der in the discourse, using multiple sequence alignment with a …...

We propose a complete probabilistic discrim- inative framework for performing sentence- level discourse analysis. Our framework com- prises a discourse segmenter, based on a bi- nary classifier, and a discourse parser, which applies an optimal CKY-like parsing algo- rithm to probabilities inferred from a Dynamic Conditional Random Field. We show …...

Entity linking systems link noun-phrase men- tions in text to their corresponding Wikipedia articles. However, NLP applications would gain from the ability to detect and type all entities mentioned in text, including the long tail of entities not prominent enough to have their own Wikipedia articles. In this paper we …...

Because the real world evolves over time, nu- merous relations between entities written in presently available texts are already obsolete or will potentially evolve in the future. This study aims at resolving the intricacy in con- sistently compiling relations extracted from text, and presents a method for identifying constancy and …...

We present a distantly supervised system for extracting the temporal bounds of fluents (re- lations which only hold during certain times, such as attends school). Unlike previous pipelined approaches, our model does not as- sume independence between each fluent or even between named entities with known con- nections (parent, spouse, …...

We describe a transformation-based learning method for learning a sequence of mono- lingual tree transformations that improve the agreement between constituent trees and word alignments in bilingual corpora. Using the manually annotated English Chinese Transla- tion Treebank, we show how our method au- tomatically discovers transformations that ac- commodate differences …...