Articles

This paper introduces a new algorithm to parse discourse within the framework of Rhetorical Structure Theory (RST). Our method is based on recent advances in the field of statistical machine learning (mul- tivariate capabilities of Support Vector Machines) and a rich feature space. RST offers a formal framework for hierarchical …...

We aim to shed light on the state-of-the-art in NP coreference resolution by teasing apart the differ- ences in the MUC and ACE task definitions, the as- sumptions made in evaluation methodologies, and inherent differences in text corpora. First, we exam- ine three subproblems that play a role in coreference …...

This paper explores how to apply the notion of caching introduced by Walker (1996) to the task of zero-anaphora resolution. We propose a machine learning-based imple- mentation of a cache model to reduce the computational cost of identifying an an- tecedent. Our empirical evaluation with Japanese newspaper articles shows that …...

Recent advances in functional Magnetic Resonance Imaging (fMRI) offer a significant new approach to studying semantic represen- tations in humans by making it possible to di- rectly observe brain activity while people comprehend words and sentences. In this study, we investigate how humans compre- hend adjective-noun phrases (e.g. strong dog) …...

This paper describes our system for generating Chinese aspect expressions. In the system, the semantics of different aspects is characterized by specific temporal and conceptual features. The semantic applicability conditions of each individual aspect are theoretically represented by an aspect selection function (ASF). The generation is realized by evaluating implemented …...

This paper presents a set of Bayesian methods for automatically extending the WORDNET ontology with new concepts and annotating existing concepts with generic property fields, or attributes. We base our approach on Latent Dirichlet Al- location and evaluate along two dimen- sions: (1) the precision of the ranked lists of …...

We present a new approach to learning a semantic parser (a system that maps natu- ral language sentences into logical form). Unlike previous methods, it exploits an ex- isting syntactic parser to produce disam- biguated parse trees that drive the compo- sitional semantic interpretation. The re- sulting system produces improved …...

We describe an unsupervised system for learn- ing narrative schemas, coherent sequences or sets of events (arrested(POLICE,SUSPECT), convicted( JUDGE, SUSPECT)) whose arguments are filled with participant semantic roles defined over words (JUDGE = {judge, jury, court}, POLICE = {police, agent, authorities}). Unlike most previous work in event structure or semantic …...

Statistical models in machine translation exhibit spurious ambiguity. That is, the probability of an output string is split among many distinct derivations (e.g., trees or segmentations). In principle, the goodness of a string is measured by the total probability of its many derivations. However, finding the best string (e.g., dur- …...

This paper presents collaborative decoding (co-decoding), a new method to improve ma- chine translation accuracy by leveraging trans- lation consensus between multiple machine translation decoders. Different from system combination and MBR decoding, which post- process the n-best lists or word lattice of ma- chine translation decoders, in our method mul- …...

The minimum Bayes risk (MBR) decoding ob- jective improves BLEU scores for machine trans- lation output relative to the standard Viterbi ob- jective of maximizing model score. However, MBR targeting BLEU is prohibitively slow to op- timize over k-best lists for large k. In this pa- per, we introduce and …...

Current SMT systems usually decode with single translation models and cannot ben- efit from the strengths of other models in decoding phase. We instead propose joint decoding, a method that combines multi- ple translation models in one decoder. Our joint decoder draws connections among multiple models by integrating the trans- …...

Current tree-to-tree models suffer from parsing errors as they usually use only 1- best parses for rule extraction and decod- ing. We instead propose a forest-based tree-to-tree model that uses packed forests. The model is based on a probabilis- tic synchronous tree substitution gram- mar (STSG), which can be learned …...

This paper presents a model for summa- rizing multiple untranscribed spoken doc- uments. Without assuming the availabil- ity of transcripts, the model modifies a recently proposed unsupervised algorithm to detect re-occurring acoustic patterns in speech and uses them to estimate similari- ties between utterances, which are in turn used to …...

To support the real-time understanding of spoken monologue such as lectures and commentaries, the development of a cap- tioning system is required. In monologues, since a sentence tends to be long, each sentence is often displayed in multi lines on one screen, it is necessary to insert linefeeds into a …...

Most of previous approaches to automatic prosodic event detection are based on su- pervised learning, relying on the avail- ability of a corpus that is annotated with the prosodic labels of interest in order to train the classification models. However, creating such resources is an expensive and time-consuming task. In …...

Manually annotated corpora are valuable but scarce resources, yet for many anno- tation tasks such as treebanking and se- quence labeling there exist multiple cor- pora with different and incompatible anno- tation guidelines or standards. This seems to be a great waste of human efforts, and it would be nice …...

Supervised sequence-labeling systems in natural language processing often suffer from data sparsity because they use word types as features in their prediction tasks. Consequently, they have difficulty estimat- ing parameters for types which appear in the test set, but seldom (or never) ap- pear in the training set. We demonstrate …...

We present a global joint model for lemmatization and part-of-speech predic- tion. Using only morphological lexicons and unlabeled data, we learn a partially- supervised part-of-speech tagger and a lemmatizer which are combined using fea- tures on a dynamically linked dependency structure of words. We evaluate our model on English, Bulgarian, …...

We present a novel approach to decid- ing whether two sentences hold a para- phrase relationship. We employ a gen- erative model that generates a paraphrase of a given sentence, and we use proba- bilistic inference to reason about whether two sentences share the paraphrase rela- tionship. The model cleanly …...