Articles

We describe our experiments with training algorithms for tree-to-tree synchronous tree-substitution grammar (STSG) for monolingual translation tasks such as sentence compression and paraphrasing. These translation tasks are characterized by the relative ability to commit to parallel parse trees and availability of word align- ments, yet the unavailability of large-scale data, …...

Automated summarization methods can be defined as “language-independent,” if they are not based on any language- specific knowledge. Such methods can be used for multilingual summarization defined by Mani (2001) as “processing several languages, with summary in the same language as input.” In this pa- per, we introduce MUSE, a …...

Cross-language document summarization is a task of producing a summary in one language for a document set in a different language. Ex- isting methods simply use machine translation for document translation or summary transla- tion. However, current machine translation services are far from satisfactory, which re- sults in that the …...

We propose CMSMs, a novel type of generic compositional models for syntac- tic and semantic aspects of natural lan- guage, based on matrix multiplication. We argue for the structural and cognitive plau- sibility of this model and show that it is able to cover and combine various com- mon compositional …...

This paper provides a unified, learning- theoretic analysis of several learnable classes of languages discussed previously in the literature. The analysis shows that for these classes an incremental, globally consistent, locally conservative, set-driven learner always exists. Additionally, the analysis provides a recipe for constructing new learnable classes. Potential applica- tions …...

Strictly Piecewise (SP) languages are a subclass of regular languages which en- code certain kinds of long-distance de- pendencies that are found in natural lan- guages. Like the classes in the Chom- sky and Subregular hierarchies, there are many independently converging character- izations of the SP class (Rogers et al., …...

This paper proposes a convolution forest ker- nel to effectively explore rich structured fea- tures embedded in a packed parse forest. As opposed to the convolution tree kernel, the proposed forest kernel does not have to com- mit to a single best parse tree, is thus able to explore very …...

In this paper, we systematically assess the value of using web-scale N-gram data in state-of-the-art supervised NLP classifiers. We compare classifiers that include or ex- clude features for the counts of various N-grams, where the counts are obtained from a web-scale auxiliary corpus. We show that including N-gram count features …...

We explore how to improve machine trans- lation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing re- turns that is commonly encountered. We present an active learning-style data solic- itation algorithm to meet …...

Wikipedia articles in different languages are connected by interwiki links that are increasingly being recognized as a valu- able source of cross-lingual information. Unfortunately, large numbers of links are imprecise or simply wrong. In this pa- per, techniques to detect such problems are identified. We formalize their removal as an …...

This paper proposes new algorithms to com- pute the sense similarity between two units (words, phrases, rules, etc.) from parallel cor- pora. The sense similarity scores are computed by using the vector space model. We then ap- ply the algorithms to statistical machine trans- lation by computing the sense similarity …...

 This paper proposes to use monolingual collocations to improve Statistical Ma- chine Translation (SMT). We make use of the collocation probabilities, which are estimated from monolingual corpora, in two aspects, namely improving word alignment for various kinds of SMT sys- tems and improving phrase table for phrase-based SMT. The …...

Scoring sentences in documents given ab- stract summaries created by humans is im- portant in extractive multi-document sum- marization. In this paper, we formulate ex- tractive summarization as a two step learn- ing problem building a generative model for pattern discovery and a regression model for inference. We calculate scores …...

We present a system that learns to fol- low navigational natural language direc- tions. Where traditional models learn from linguistic annotation or word distri- butions, our approach is grounded in the world, learning by apprenticeship from routes through a map paired with English descriptions. Lacking an explicit align- ment between …...

In-vehicle dialogue systems often contain more than one application, e.g. a navi- gation and a telephone application. This means that the user might, for example, in- terrupt the interaction with the telephone application to ask for directions from the navigation application, and then resume the dialogue with the telephone applica- …...

Event extraction is a particularly challenging type of information extraction (IE). Most current event extraction systems rely on local information at the phrase or sentence level. However, this local context may be insufficient to resolve ambiguities in identifying particular types of events; information from a wider scope can serve to …...

Letter-phoneme alignment is usually gen- erated by a straightforward application of the EM algorithm. We explore several al- ternative alignment methods that employ phonetics, integer programming, and sets of constraints, and propose a novel ap- proach of refining the EM alignment by aggregation of best alignments. We per- form both …...

In recent years, research in natural language processing has increasingly focused on normalizing SMS messages. Different well-defined approaches have been proposed, but the problem remains far from being solved: best systems achieve a 11% Word Error Rate. This paper presents a method that shares similarities with both spell checking and …...

This paper presents a framework for au- tomatically processing information com- ing from community Question Answering (cQA) portals with the purpose of gen- erating a trustful, complete, relevant and succinct summary in response to a ques- tion. We exploit the metadata intrinsically present in User Generated Content (UGC) to bias …...

Prior use of machine learning in genre classification used a list of labels as clas- sification categories. However, genre classes are often organised into hierar- chies, e.g., covering the subgenres of fic- tion. In this paper we present a method of using the hierarchy of labels to improve the classification …...