Articles

Disambiguating concepts and entities in a con- text sensitive way is a fundamental problem in natural language processing. The compre- hensiveness of Wikipedia has made the on- line encyclopedia an increasingly popular tar- get for disambiguation. Disambiguation to Wikipedia is similar to a traditional Word Sense Disambiguation task, but distinct …...

We present a method to discover robust and interpretable sociolinguistic associations from raw geotagged text data. Using aggregate de- mographic statistics about the authors’ geo- graphic communities, we solve a multi-output regression problem between demographics and lexical frequencies. By imposing a com- posite ℓ1,∞ regularizer, we obtain structured sparsity, driving …...

We propose a novel unsupervised method for separating out distinct authorial compo- nents of a document. In particular, we show that, given a book artificially “munged” from two thematically similar biblical books, we can separate out the two consti- tuent books almost perfectly. This allows us to automatically recapitulate many …...

In this paper we address the problem of ques- tion recommendation from large archives of community question answering data by ex- ploiting the users’ information needs. Our experimental results indicate that questions based on the same or similar information need can provide excellent question recommenda- tion. We show that translation …...

Resolving coordination ambiguity is a clas- sic hard problem. This paper looks at co- ordination disambiguation in complex noun phrases (NPs). Parsers trained on the Penn Treebank are reporting impressive numbers these days, but they don’t do very well on this problem (79%). We explore systems trained using three types …...

This paper explores the use of bilingual par- allel corpora as a source of lexical knowl- edge for cross-lingual textual entailment. We claim that, in spite of the inherent difficul- ties of the task, phrase tables extracted from parallel data allow to capture both lexical re- lations between single words, …...

We present a first known result of high pre- cision rare word bilingual extraction from comparable corpora, using aligned compara- ble documents and supervised classification. We incorporate two features, a context-vector similarity and a co-occurrence model between words in aligned documents in a machine learning approach. We test our hypothesis …...

While it is has often been observed that the product of translation is somehow different than non-translated text, scholars have empha- sized two distinct bases for such differences. Some have noted interference from the source language spilling over into translation in a source-language-specific way, while others have noted general effects …...

We propose a principled and efficient phrase- to-phrase alignment model, useful in machine translation as well as other related natural lan- guage processing problems. In a hidden semi- Markov model, word-to-phrase and phrase- to-word translations are modeled directly by the system. Agreement between two direc- tional models encourages the selection …...

We propose a novel approach to translating from a morphologically complex language. Unlike previous research, which has targeted word inflections and concatenations, we fo- cus on the pairwise relationship between mor- phologically related words, which we treat as potential paraphrases and handle using para- phrasing techniques at the word, phrase, …...

In this paper, with a belief that a language model that embraces a larger context provides better prediction ability, we present two ex- tensions to standard n-gram language mod- els in statistical machine translation: a back- ward language model that augments the con- ventional forward language model, and a mu- …...

We present minimum Bayes-risk system com- bination, a method that integrates consen- sus decoding and system combination into a unified multi-system minimum Bayes-risk (MBR) technique. Unlike other MBR meth- ods that re-rank translations of a single SMT system, MBR system combination uses the MBR decision rule and a linear combina- …...

This paper presents hypothesis mixture decoding (HM decoding), a new decoding scheme that performs translation reconstruction using hypo- theses generated by multiple translation systems. HM decoding involves two decoding stages: first, each component system decodes indepen- dently, with the explored search space kept for use in the next step; second, …...

The state-of-the-art system combination method for machine translation (MT) is based on confusion networks constructed by aligning hypotheses with regard to word similarities. We introduce a novel system combination framework in which hypotheses are encoded as a confusion forest, a packed forest representing alternative trees. The forest is generated using …...

We present a discriminative learning method to improve the consistency of translations in phrase-based Statistical Machine Translation (SMT) systems. Our method is inspired by Translation Memory (TM) systems which are widely used by human translators in industrial settings. We constrain the translation of an in- put sentence using the most …...

In many natural language applications, there is a need to enrich syntactical parse trees. We present a statistical tree annotator augmenting nodes with additional information. The anno- tator is generic and can be applied to a va- riety of applications. We report 3 such ap- plications in this paper: predicting …...

Naively collecting translations by crowd- sourcing the task to non-professional trans- lators yields disfluent, low-quality results if no quality control is exercised. We demon- strate a variety of mechanisms that increase the translation quality to near professional lev- els. Specifically, we solicit redundant transla- tions and edits to them, and …...

We introduce synchronous tree adjoining grammars (TAG) into tree-to-string transla- tion, which converts a source tree to a target string. Without reconstructing TAG deriva- tions explicitly, our rule extraction algo- rithm directly learns tree-to-string rules from aligned Treebank-style trees. As tree-to-string translation casts decoding as a tree parsing problem rather …...

The availability of learner corpora, especially those which have been manually error-tagged or shallow-parsed, is still limited. This means that researchers do not have a common devel- opment and test set for natural language pro- cessing of learner English such as for gram- matical error detection. Given this back- ground, …...

We develop a novel approach to the seman- tic analysis of short text segments and demon- strate its utility on a large corpus of Web search queries. Extracting meaning from short text segments is difficult as there is little semantic redundancy between terms; hence methods based on shallow semantic analy- …...