Articles

This paper presents a generative model for the automatic discovery of relations between entities in electronic medical records. The model discovers relation instances and their types by determining which context tokens ex- press the relation. Additionally, the valid se- mantic classes for each type of relation are de- termined. We …...

We present an automatic method which lever- ages word lengthening to adapt a sentiment lexicon specifically for Twitter and similar so- cial messaging networks. The contributions of the paper are as follows. First, we call at- tention to lengthening as a widespread phe- nomenon in microblogs and social messag- ing, …...

Preordering of source side sentences has proved to be useful in improving statistical machine translation. Most work has used a parser in the source language along with rules to map the source language word order into the target language word order. The require- ment to have a source language parser …...

We present a quasi-synchronous dependency grammar (Smith and Eisner, 2006) for ma- chine translation in which the leaves of the tree are phrases rather than words as in pre- vious work (Gimpel and Smith, 2009). This formulation allows us to combine structural components of phrase-based and syntax-based MT in a …...

The Text Analysis Conference (TAC) ranks summarization systems by their average score over a collection of document sets. We in- vestigate the statistical appropriateness of this score and propose an alternative that better distinguishes between human and machine evaluation systems. 1...

We propose an algorithm allowing to effi- ciently retrieve example treelets in a parsed tree database in order to allow on-the-fly ex- traction of syntactic translation rules. We also propose improvements of this algorithm al- lowing several kinds of flexible matchings. 1...

We investigate an important and challeng- ing problem in summary generation, i.e., Evolutionary Trans-Temporal Summarization (ETTS), which generates news timelines from massive data on the Internet. ETTS greatly facilitates fast news browsing and knowl- edge comprehension, and hence is a neces- sity. Given the collection of time-stamped web documents related …...

Conversations provide rich opportunities for interactive, continuous learning. When some- thing goes wrong, a system can ask for clari- fication, rewording, or otherwise redirect the interaction to achieve its goals. In this pa- per, we present an approach for using con- versational interactions of this type to induce semantic parsers. …...

In order to obtain a fine-grained evaluation of parser accuracy over naturally occurring text, we study 100 examples each of ten reason- ably frequent linguistic phenomena, randomly selected from a parsed version of the En- glish Wikipedia. We construct a correspond- ing set of gold-standard target dependencies for these 1000 …...

Many machine translation evaluation met- rics have been proposed after the seminal BLEU metric, and many among them have been found to consistently outperform BLEU, demonstrated by their better correlations with human judgment. It has long been the hope that by tuning machine translation systems against these new generation metrics, …...

We investigate the differences between language models compiled from original target-language texts and those compiled from texts manually translated to the tar- get language. Corroborating established observations of Translation Studies, we demonstrate that the latter are signifi- cantly better predictors of translated sen- tences than the former, and hence fit …...

We explore efficient domain adaptation for the task of statistical machine translation based on extracting sentences from a large general- domain parallel corpus that are most relevant to the target domain. These sentences may be selected with simple cross-entropy based methods, of which we present three. As these sentences are …...

We present a system for the large scale in- duction of cognate groups. Our model ex- plains the evolution of cognates as a sequence of mutations and innovations along a phy- logeny. On the task of identifying cognates from over 21,000 words in 218 different lan- guages from the Oceanic …...

Log-linear parsing models are often trained by optimizing likelihood, but we would prefer to optimise for a task-specific metric like F- measure. Softmax-margin is a convex objec- tive for such models that minimises a bound on expected risk for a given loss function, but its na¨ıve application requires the loss …...

Text simplification aims to rewrite text into simpler versions, and thus make information accessible to a broader audience. Most pre- vious work simplifies sentences using hand- crafted rules aimed at splitting long sentences, or substitutes difficult words using a prede- fined dictionary. This paper presents a data- driven model based …...

In this paper, we consider the problem of un- supervised morphological analysis from a new angle. Past work has endeavored to design un- supervised learning methods which explicitly or implicitly encode inductive biases appropri- ate to the task at hand. We propose instead to treat morphological analysis as a structured …...

This paper introduces a psycholinguistic model of sentence processing which combines a Hidden Markov Model noun phrase chun- ker with a co-reference classifier. Both mod- els are fully incremental and generative, giv- ing probabilities of lexical elements condi- tional upon linguistic structure. This allows us to compute the information theoretic …...

We present a simple objective function that when optimized yields accurate solutions to both decipherment and cognate pair identifica- tion problems. The objective simultaneously scores a matching between two alphabets and a matching between two lexicons, each in a different language. We introduce a simple coordinate descent procedure that efficiently …...

This paper develops a minimally supervised approach, based on focused distributional sim- ilarity methods and discourse connectives, for identifying of causality relations between events in context. While it has been shown that distributional similarity can help identify- ing causality, we observe that discourse con- nectives and the particular discourse relation …...

This paper presents a new algorithm for lin- ear text segmentation. It is an adaptation of Affinity Propagation, a state-of-the-art clus- tering algorithm in the framework of factor graphs. Affinity Propagation for Segmenta- tion, or APS, receives a set of pairwise simi- larities between data points and produces seg- ment …...