Articles

How far can we get with unsupervised parsing if we make our training corpus several orders of magnitude larger than has hitherto be attempted? We present a new algorithm for unsupervised parsing using an all-subtrees model, termed U-DOP*, which parses directly with packed forests of all binary trees. We train …...

This paper introduces a Maximum Entropy dependency parser based on an efficient k- best Maximum Spanning Tree (MST) algo- rithm. Although recent work suggests that the edge-factored constraints of the MST al- gorithm significantly inhibit parsing accu- racy, we show that generating the 50-best parses according to an edge-factored model …...

In order to effectively access the rapidly increasing range of media content available in the home, new kinds of more natural in- terfaces are needed. In this paper, we ex- plore the application of multimodal inter- face technologies to searching and brows- ing a database of movies. The resulting system …...

This paper describes an incremental parser and an unsupervised learning algorithm for inducing this parser from plain text. The parser uses a representation for syntactic structure similar to dependency links which is well-suited for incremental parsing. contrast to previous unsupervised parsers,In the parser does not use part-of-speech tags and both …...

In this paper we explore the utility of the Navigation Map (NM), a graphical repre- sentation of the discourse structure. We run a user study to investigate if users perceive the NM as helpful in a tutoring spoken dia- logue system. From the users’ perspective, our results show that the …...

Motivated by psycholinguistic findings that eye gaze is tightly linked to human lan- guage production, we developed an unsuper- vised approach based on translation models to automatically learn the mappings between words and objects on a graphic display dur- ing human machine conversation. The ex- perimental results indicate that user …...

Non-verbal modalities such as gesture can improve processing of spontaneous spoken language. For example, similar hand ges- tures tend to predict semantic similarity, so features that quantify gestural similarity can improve semantic tasks such as coreference resolution. However, not all hand move- ments are informative gestures; psycholog- ical research has …...

We translate sentence generation from TAG grammars with semantic and pragmatic in- formation into a planning problem by encod- ing the contribution of each word declara- tively and explicitly. This allows us to ex- ploit the performance of off-the-shelf plan- ners. It also opens up new perspectives on referring expression …...

Surface realisers divide into those used in generation (NLG geared realisers) and those mirroring the parsing process (Reversible re- alisers). While the first rely on grammars not easily usable for parsing, it is unclear how the second type of realisers could be param- eterised to yield from among the set …...

We investigate the factors which determine constituent order in German clauses and pro- pose an algorithm which performs the task in two steps: First, the best candidate for the initial sentence position is chosen. Then, the order for the remaining constituents is determined. The first task is more difficult than …...

Recently, confusion network decoding has been applied in machine translation system combination. Due to errors in the hypoth- esis alignment, decoding may result in un- grammatical combination outputs. This pa- per describes an improved confusion net- work based method to combine outputs from multiple MT systems. In this approach, ar- …...

Many automatic evaluation metrics for ma- chine translation (MT) rely on making com- parisons to human translations, a resource that may not always be available. We present a method for developing sentence-level MT evaluation metrics that do not directly rely on human reference translations. Our met- rics are developed using …...

We introduce a simple method to pack words for statistical word alignment. Our goal is to simplify the task of automatic word align- ment by packing several consecutive words together when we believe they correspond to a single word in the opposite language. This is done using the word aligner …...

Until quite recently, extending Phrase-based Statistical Machine Translation (PBSMT) with syntactic structure caused system per- formance to deteriorate. In this work we show that incorporating lexical syntactic de- scriptions in the form of supertags can yield significantly better PBSMT systems. We de- scribe a novel PBSMT model that integrates supertags …...

In evaluating the output of language tech- nology applications—MT, natural language generation, summarisation—automatic eval- uation techniques generally conflate mea- surement of faithfulness to source content with fluency of the resulting text. In this paper we develop an automatic evaluation metric to estimate fluency alone, by examin- ing the use of …...

Over the last few years, two of the main research directions in machine learning of natural language processing have been the study of semi-supervised learning algo- rithms as a way to train classifiers when the labeled data is scarce, and the study of ways to exploit knowledge and global information …...

Historically, unsupervised learning tech- niques have lacked a principled technique for selecting the number of unseen compo- nents. Research into non-parametric priors, such as the Dirichlet process, has enabled in- stead the use of infinite models, in which the number of hidden categories is not fixed, but can grow with …...

Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem from the instance weighting per- spective. We formally analyze and charac- terize the domain adaptation problem from a distributional view, …...

We describe an approach to domain adapta- tion that is appropriate exactly in the case when one has enough “target” data to do slightly better than just using only “source” data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms state- …...

We present a web mining method for discov- ering and enhancing relationships in which a specified concept (word class) participates. We discover a whole range of relationships focused on the given concept, rather than generic known relationships as in most pre- vious work. Our method is based on cluster- ing …...