Articles

Social media such as Twitter have become an important method of communication, with potential opportunities for NLG to fa- cilitate the generation of social media con- tent. We focus on the generation of in- dicative tweets that contain a link to an ex- ternal web page. While it is natural …...

We provide an analysis of current evalua- tion methodologies applied to summariza- tion metrics and identify the following ar- eas of concern: (1) movement away from evaluation by correlation with human as- sessment; (2) omission of important com- ponents of human assessment from eval- uations, in addition to large numbers …...

The task of cross-language document summarization is to create a summary in a target language from documents in a dif- ferent source language. Previous meth- ods only involve direct extraction of au- tomatically translated sentences from the original documents. Inspired by phrase- based machine translation, we propose a phrase-based model …...

We present a novel framework of system combination for multi-document summarization. For each input set (input), we generate candidate summaries by combining whole sentences from the summaries generated by different systems. We show that the oracle among these candidates is much better than the summaries that we have combined. We …...

Trending topics in microblogs such as Twitter are valuable resources to under- stand social aspects of real-world events. To enable deep analyses of such trends, se- mantic annotation is an effective approach; yet the problem of annotating microblog trending topics is largely unexplored by the research community. In this work, …...

In this paper, we present a comprehensive study of the relationship between an indi- vidual’s personal traits and his/her brand preferences. In our analysis, we included a large number of character traits such as personality, personal values and individual needs. These trait features were obtained from both a psychometric survey …...

Text data has recently been used as evi- dence in estimating the political ideologies of individuals, including political elites and social media users. While inferences about people are often the intrinsic quan- tity of interest, we draw inspiration from open information extraction to identify a new task: inferring the political …...

This paper describes an approach to large- scale modeling of sentiment analysis for the social sciences. The goal is to model relations between nation states through so- cial media. Many cross-disciplinary appli- cations of NLP involve making predictions (such as predicting political elections), but this paper instead focuses on a …...

Divergent word order between languages causes delay in simultaneous machine translation. We present a sentence rewrit- ing method that generates more mono- tonic translations to improve the speed- accuracy tradeoff. We design grammati- cality and meaning-preserving syntactic transformation rules that operate on con- stituent parse trees. We apply the rules …...

We present a novel approach for unsu- pervised induction of a Reordering Gram- mar using a modified form of permuta- tion trees (Zhang and Gildea, 2007), which we apply to preordering in phrase-based machine translation. Unlike previous ap- proaches, we induce in one step both the hierarchical structure and the …...

Compared to tree grammars, graph gram- mars have stronger generative capacity over structures. Based on an edge re- placement grammar, in this paper we pro- pose to use a synchronous graph-to-string grammar for statistical machine transla- tion. The graph we use is directly con- verted from a dependency tree by …...

Distributional methods have proven to ex- cel at capturing fuzzy, graded aspects of meaning (Italy is more similar to Spain than to Germany). In contrast, it is diffi- cult to extract the values of more specific attributes of word referents from distribu- tional representations, attributes of the kind typically found …...

In this paper, we consider the task of learn- ing control policies for text-based games. In these games, all interactions in the vir- tual world are through text and the un- derlying state is not observed. The re- sulting language barrier makes such envi- ronments challenging for automatic game players. …...

In this paper, we introduce an approach to au- tomatically map a standard distributional se- mantic space onto a set-theoretic model. We predict that there is a functional relationship between distributional information and vecto- rial concept representations in which dimen- sions are predicates and weights are gener- alised quantifiers. In …...

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained mod- els of vector-space representations. Yet while ‘multi-sense’ methods have been proposed and tested on artificial word- similarity tasks, we don’t know if they im- prove real natural language understanding tasks. In …...

Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usabil- ity and perceived quality. Most NLG sys- tems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural vari- ation of human …...

Machine comprehension of text is the overarching goal of a great deal of re- search in natural language processing. The Machine Comprehension Test (Richard- son et al., 2013) was recently proposed to assess methods on an open-domain, exten- sible, and easy-to-evaluate task consisting of two datasets. In this paper we …...

We propose a grammar induction tech- nique for AMR semantic parsing. While previous grammar induction techniques were designed to re-learn a new parser for each target application, the recently anno- tated AMR Bank provides a unique op- portunity to induce a single model for un- derstanding broad-coverage newswire text and …...

Word embeddings encode semantic mean- ings of words into low-dimension word vectors. In most word embeddings, one cannot interpret the meanings of specific dimensions of those word vectors. Non- negative matrix factorization (NMF) has been proposed to learn interpretable word embeddings via non-negative constraints. However, NMF methods suffer from scale …...

Previous studies have shown that health reports in social media, such as Dai- lyStrength and Twitter, have potential for monitoring health conditions (e.g. adverse drug reactions, infectious diseases) in par- ticular communities. However, in order for a machine to understand and make in- ferences on these health conditions, the ability …...