Articles

We discuss and analyze the problem of find- ing a distribution that minimizes the relative entropy to a prior distribution while satisfying max-norm constraints with respect to an ob- served distribution. This setting generalizes the classical maximum entropy problems as it relaxes the standard constraints on the ob- served values. …...

Word boundaries within noun compounds are not marked by white spaces in a number of languages, unlike in English, and it is benefi- cial for various NLP applications to split such noun compounds. In the case of Japanese, noun compounds made up of katakana words (i.e., transliterated foreign words) are …...

Mapping documents into an interlingual rep- resentation can help bridge the language bar- rier of cross-lingual corpora. Many existing approaches are based on word co-occurrences extracted from aligned training data, repre- sented as a covariance matrix. In theory, such a covariance matrix should represent seman- tic equivalence, and should be …...

Discriminative training for machine transla- tion has been well studied in the recent past. A limitation of the work to date is that it relies on the availability of high-quality in-domain bilingual text for supervised training. We present an unsupervised discriminative train- ing framework to incorporate the usually plen- tiful …...

In active dual supervision, not only informa- tive examples but also features are selected for labeling to build a high quality classifier with low cost. However, how to measure the infor- mativeness for both examples and feature on the same scale has not been well solved. In this paper, we …...

Statistical machine translation systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring document-level information. In this paper, we propose a cache-based approach to document-level translation. Since caches mainly depend on relevant data to supervise subsequent decisions, it is critical …...

Models of word alignment built as sequences of links have limited expressive power, but are easy to decode. Word aligners that model the alignment matrix can express arbitrary align- ments, but are difficult to decode. We pro- pose an alignment matrix model as a cor- rection algorithm to an underlying …...

State of the art Tree Structures Prediction techniques rely on bottom-up decoding. These approaches allow the use of context-free fea- tures and bottom-up features. We discuss the limitations of mainstream techniques in solving common Natural Language Process- ing tasks. Then we devise a new framework that goes beyond Bottom-up Decoding, …...

Although discriminative training guarantees to improve statistical machine translation by in- corporating a large amount of overlapping fea- tures, it is hard to scale up to large data due to decoding complexity. We propose a new al- gorithm to generate translation forest of train- ing data in linear time with …...

Part-of-speech language modeling is com- monly used as a component in statistical ma- chine translation systems, but there is mixed evidence that its usage leads to significant im- provements. We argue that its limited effec- tiveness is due to the lack of lexicalization. We introduce a new approach that builds …...

Long-distance reordering remains one of the biggest challenges facing machine translation. We derive soft constraints from the source de- pendency parsing to directly address the re- ordering problem for the hierarchical phrase- based model. Our approach significantly im- proves Chinese–English machine translation on a large-scale task by 0.84 BLEU points …...

We propose an architecture for expressing various linguistically-motivated features that help identify multi-word expressions in nat- ural language texts. The architecture com- bines various linguistically-motivated clas- sification features in a Bayesian Network. We introduce novel ways for computing many of these features, and manually de- fine linguistically-motivated interrelationships among them, …...

This paper proposes a semi-supervised rela- tion acquisition method that does not rely on extraction patterns (e.g. “X causes Y” for causal relations) but instead learns a combi- nation of indirect evidence for the target re- lation — semantic word classes and partial patterns. This method can extract long tail …...

In this paper we present a novel approach to entity linking based on a statistical lan- guage model-based information retrieval with query expansion. We use both local con- texts and global world knowledge to expand query language models. We place a strong emphasis on named entities in the local con- …...

We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candi- date slot fillers to identify meaningful tem- plate slots. We propose a generative model that incorporates distributional prior knowl- edge to help distribute candidates in a docu- ment into appropriate …...

Most NLP systems use tokenization as part of preprocessing. Generally, tokenizers are based on simple heuristics and do not recog- nize multi-word units (MWUs) like hot dog or black hole unless a precompiled list of MWUs is available. In this paper, we propose a new cascaded model for detecting MWUs …...

Disambiguating named entities in natural- language text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO. This paper presents a ro- bust method for collective disambiguation, by harnessing context from knowledge bases and using a new form …...

In this paper, we present a new ranking scheme, collaborative ranking (CR). In con- trast to traditional non-collaborative ranking scheme which solely relies on the strengths of isolated queries and one stand-alone rank- ing algorithm, the new scheme integrates the strengths from multiple collaborators of a query and the strengths …...

An A-C bilingual dictionary can be inferred by merging A-B and B-C dictionaries using B as pivot. However, polysemous pivot words often produce wrong translation candidates. This paper analyzes two methods for pruning wrong candidates: one based on exploiting the structure of the source dictionaries, and the other based on …...

Information-oriented document labeling is a special document multi-labeling task where the target labels refer to a specific information instead of the topic of the whole document. These kind of tasks are usually solved by look- ing up indicator phrases and analyzing their local context to filter false positive matches. Here, …...