Articles

A survey of existing methods for stopping ac- tive learning (AL) reveals the needs for meth- ods that are: more widely applicable; more ag- gressive in saving annotations; and more sta- ble across changing datasets. A new method for stopping AL based on stabilizing predic- tions is presented that addresses …...

The combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two ma- jor drawbacks: first, the implicit representa- tion of feature spaces does not allow us to un- derstand which features actually triggered the generalization; second, the resulting compu- tational burden …...

Finding negation signals and their scope in text is an important subtask in information ex- traction. In this paper we present a machine learning system that finds the scope of nega- tion in biomedical texts. The system combines several classifiers and works in two phases. To investigate the robustness of …...

We present a simple but very effective ap- proach to identifying high-quality data in noisy data sets for structured problems like parsing, by greedily exploiting partial struc- tures. We analyze our approach in an anno- tation projection framework for dependency trees, and show how dependency parsers from two different paradigms …...

Creating large amounts of manually annotated training data for statistical parsers imposes heavy cognitive load on the human annota- tor and is thus costly and error prone. is hence of high importance to decrease theIt human efforts involved in creating training data without harming parser performance. For constituency parsers, these …...

This paper addresses the problem of ex- tracting the most important facts from a news article. Our approach uses syntac- tic, semantic, and general statistical fea- tures to identify the most important sen- tences in a document. The importance of the individual features is estimated us- ing generalized iterative scaling …...

We propose a novel machine learning task that consists in learning to predict which words in a text are fixated by a reader. In a first pilot experiment, we show that it is possible to out- perform a majority baseline using a transition- based model with a logistic regression classi- …...

Theories of human language acquisition as- sume that learning to understand sentences is a partially-supervised task (at best). Instead of using ‘gold-standard’ feedback, we train a simplified “Baby” Semantic Role Labeling system by combining world knowledge and simple grammatical constraints to form a po- tentially noisy training signal. This combina- …...

This work introduces a model free approach to sentence compression, which grew out of ideas from Nomoto (2008), and examines how it com- pares to a state-of-art model intensive approach known as Tree-to-Tree Transducer, or T3 (Cohn and Lapata, 2008). It is found that a model free approach significantly outperforms …...

Untranslated words still constitute a ma- jor problem for Statistical Machine Trans- lation (SMT), and current SMT systems are limited by the quantity of parallel training texts. Augmenting the training data with paraphrases generated by pivot- ing through other languages alleviates this problem, especially for the so-called “low density” languages. …...

Tree based translation models are a com- pelling means of integrating linguistic in- formation into machine translation. Syn- tax can inform lexical selection and re- ordering choices and thereby improve translation quality. Research to date has focussed primarily on decoding with such models, but less on the difficult problem of …...

In this work we present a novel technique to rescore fragments in the Data-Oriented Translation model based on their contri- bution to translation accuracy. We de- scribe three new rescoring methods, and present the initial results of a pilot experi- ment on a small subset of the Europarl cor- pus. …...

Binarization of Synchronous Context Free Grammars (SCFG) is essential for achieving polynomial time complexity of decoding for SCFG parsing based machine translation sys- tems. In this paper, we first investigate the excess edge competition issue caused by a left- heavy binary SCFG derived with the method of Zhang et al. …...

Automatically detecting human social in- tentions from spoken conversation is an important task for dialogue understand- ing. Since the social intentions of the speaker may differ from what is perceived by the hearer, systems that analyze human conversations need to be able to extract both the perceived and the intended …...

We present an implicit discourse relation classifier in the Penn Discourse Treebank (PDTB). Our classifier considers the con- text of the two arguments, word pair infor- mation, as well as the arguments’ internal constituent and dependency parses. Our results on the PDTB yields a significant 14.1% improvement over the baseline. …...

This paper presents an effective method for generating natural language sentences from their underlying meaning represen- tations. The method is built on top of a hybrid tree representation that jointly encodes both the meaning representation as well as the natural language in a tree structure. By using a tree conditional …...

We propose a novel unsupervised approach for distinguishing literal and non-literal use of idiomatic expressions. Our model com- bines an unsupervised and a supervised classifier. The former bases its decision on the cohesive structure of the context and labels training data for the latter, which can then take a larger …...

A number of recent publications have made use of the incremental output of stochastic parsers to derive measures of high utility for psycholinguistic modeling, following the work of Hale (2001; 2003; 2006). In this paper, we present novel methods for calculating separate lexical and syntactic surprisal measures from a single …...

We present a fully automatic method for content selection evaluation in summariza- tion that does not require the creation of human model summaries. Our work capi- talizes on the assumption that the distribu- tion of words in the input and an informa- tive summary of that input should be sim- …...

Machine involvement has the potential to speed up language documentation. We as- sess this potential with timed annotation experiments that consider annotator exper- tise, example selection methods, and sug- gestions from a machine classifier. We find that better example selection and la- bel suggestions improve efficiency, but ef- fectiveness depends …...