Articles

Current approaches to cross-language doc- ument retrieval and categorization are based on discriminative methods which represent documents in a low-dimensional vector space. In this paper we pro- pose a shift from the supervised to the knowledge-based paradigm and provide a document similarity measure which draws on BabelNet, a large multilingual …...

ion for Long-Distance Reordering in Statistical Machine Translation Chenchen Ding Department of Computer Science University of Tsukuba 1-1-1 Tennodai, Tsukuba, Ibaraki , Japan tei@mibel.cs.tsukuba.ac.jp Yuki Arase Microsoft Research No. 5 Danling St., Haidian Dist. Beijing, P.R. China yukiar@microsoft.com Abstract Word reordering is a crucial technique in statistical machine translation in …...

The open structure of online social net- works and their uncurated nature give rise to problems of user credibility and influ- ence. In this paper, we address the task of predicting the impact of Twitter users based only on features under their direct control, such as usage statistics and the …...

Recent human evaluation of machine translation has focused on relative pref- erence judgments of translation quality, making it difficult to track longitudinal im- provements over time. We carry out a large-scale crowd-sourcing experiment to estimate the degree to which state-of-the- art performance in machine translation has increased over the past …...

Using machine translation output as a starting point for human translation has become an increasingly common applica- tion of MT. We propose and evaluate three computationally efficient online methods for updating statistical MT systems in a scenario where post-edited MT output is constantly being returned to the system: (1) adding …...

Previous methods for extracting attributes (e.g., capital, population) of classes (Em- pires) from Web documents or search queries assume that relevant attributes oc- cur verbatim in the source text. The ex- tracted attributes are short phrases that correspond to quantifiable properties of various instances (ottoman empire, ro- man empire, mughal …...

Opinions may be expressed implicitly via inference over explicit sentiments and events that positively/negatively affect en- tities (goodFor/badFor events). We in- vestigate how such inferences may be exploited to improve sentiment analysis, given goodFor/badFor event information. We apply Loopy Belief Propagation to propagate sentiments among entities. The graph-based model improves …...

We present an algorithm for incremental statistical parsing with Parallel Multiple Context-Free Grammars (PMCFG). This is an extension of the algorithm by An- gelov (2009) to which we added statisti- cal ranking. We show that the new al- gorithm is several times faster than other statistical PMCFG parsing algorithms on …...

Verb errors are some of the most com- mon mistakes made by non-native writers of English but some of the least studied. The reason is that dealing with verb er- rors requires a new paradigm; essentially all research done on correcting grammat- ical errors assumes a closed set of trig- …...

We propose the design of deterministic constituent parsers that choose parser ac- tions according to the probabilities of parses of a given probabilistic context-free grammar. Several variants are presented. One of these deterministically constructs a parse structure while postponing commit- ment to labels. We investigate theoretical time complexities and report …...

Translating text from diverse sources poses a challenge to current machine translation systems which are rarely adapted to structure beyond corpus level. We explore topic adaptation on a diverse data set and present a new bilingual vari- ant of Latent Dirichlet Allocation to com- pute topic-adapted, probabilistic phrase translation features. …...

In this paper the word prediction system Soothsayer1 is described. This system pre- dicts what a user is going to write as he is keying it in. The main innovation of Soothsayer is that it not only uses idi- olects, the language of one individual per- son, as its source …...

Approaching temporal link labelling as a classification task has already been ex- plored in several works. However, choos- ing the right feature vectors to build the classification model is still an open is- sue, especially for event-event classifica- tion, whose accuracy is still under 50%. We find that using a …...

We describe a state-of-the-art automatic system that can acquire subcategorisation frames from raw text for a free word-order language. We use it to construct a subcate- gorisation lexicon of German verbs from a large Web page corpus. With an automatic verb classification paradigm we evaluate our subcategorisation lexicon against a …...

Learning algorithms for semantic parsing have improved drastically over the past decade, as steady improvements on bench- mark datasets have shown. In this pa- per we investigate whether they can gen- eralize to a novel biomedical dataset that differs in important respects from the tra- ditional geography and air travel …...

While the automatic analysis of the read- ability of texts has a long history, the use of readability assessment for text simplifi- cation has received only little attention so far. In this paper, we explore readability models for identifying differences in the reading levels of simplified and unsimpli- fied versions …...

Customers who buy products such as books online often rely on other customers reviews more than on reviews found on specialist magazines. Unfortunately the confidence in such reviews is often mis- placed due to the explosion of so-called sock puppetry–authors writing glowing reviews of their own books. Identifying such deceptive …...

Subjectivity word sense disambiguation (SWSD) is a supervised and application- specific word sense disambiguation task disambiguating between subjective and objective senses of a word. Not sur- prisingly, SWSD suffers from the knowl- edge acquisition bottleneck. In this work, we use a “cluster and label” strategy to generate labeled data for …...

We describe an approach to word ordering using modelling techniques from statisti- cal machine translation. The system in- corporates a phrase-based model of string generation that aims to take unordered bags of words and produce fluent, gram- matical sentences. We describe the gen- eration grammars and introduce parsing procedures that …...

Models of category learning have been ex- tensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper we focus on categories acquired from natural lan- guage stimuli, that is words (e.g., chair is a member of the FURNITURE category). We present a Bayesian …...