Articles

In this paper we study spectral learning methods for non-deterministic split head- automata grammars, a powerful hidden- state formalism for dependency parsing. We present a learning algorithm that, like other spectral methods, is efficient and non- susceptible to local minima. We show how this algorithm can be formulated as a …...

Probabilistic accounts of language process- ing can be psychologically tested by com- paring word-reading times (RT) to the con- ditional word probabilities estimated by language models. Using surprisal as a link- ing function, a significant correlation be- tween unlexicalized surprisal and RT has been reported (e.g., Demberg and Keller, 2008), …...

We present the PONG method to compute selectional preferences using part-of-speech (POS) N-grams. From a corpus labeled with grammatical dependencies, PONG learns the distribution of word relations for each POS N-gram. From the much larger but unlabeled Google N-grams corpus, PONG learns the distribution of POS N-grams for a given …...

Online community is an important source for latest news and information. Accurate prediction of a user’s interest can help pro- vide better user experience. In this paper, we develop a recommendation system for online forums. There are a lot of differ- ences between online forums and formal me- dia. For …...

Document revision histories are a useful and abundant source of data for natural language processing, but selecting relevant data for the task at hand is not trivial. In this paper we introduce a scalable ap- proach for automatically distinguishing be- tween factual and fluency edits in document revision histories. The …...

It is becoming clear that traditional evaluation measures used in Computational Linguistics (including Error Rates, Accuracy, Recall, Precision and F-measure) are of limited value for unbiased evaluation of systems, and are not meaningful for comparison of algorithms unless both the dataset and algorithm parameters are strictly controlled for skew (Prevalence …...

In this paper, we extend current state-of-the- art research on unsupervised acquisition of scripts, that is, stereotypical and frequently observed sequences of events. We design, evaluate and compare different methods for constructing models for script event predic- tion: given a partial chain of events in a script, predict other events …...

In this paper, we compare three different generalization methods for in-domain and cross-domain opinion holder extraction be- ing simple unsupervised word clustering, an induction method inspired by distant supervision and the usage of lexical re- sources. The generalization methods are incorporated into diverse classifiers. We show that generalization causes significant …...

We extend the original entity-based coher- ence model (Barzilay and Lapata, 2008) by learning from more fine-grained coher- ence preferences in training data. We asso- ciate multiple ranks with the set of permuta- tions originating from the same source doc- ument, as opposed to the original pairwise rankings. We also …...

In this paper, we describe a new approach to semi-supervised adaptive learning of event extraction from text. Given a set of exam- ples and an un-annotated text corpus, the BEAR system (Bootstrapping Events And Relations) will automatically learn how to recognize and understand descriptions of complex semantic relationships in text, …...

Most event extraction systems are trained with supervised learning and rely on a col- lection of annotated documents. Due to the domain-specificity of this task, event extraction systems must be retrained with new annotated data for each domain. In this paper, we propose a bootstrapping so- lution for event role …...

In this paper, we define a new type of summary for sentiment analysis: a single- sentence summary that consists of a sup- porting sentence that conveys the overall sentiment of a review as well as a convinc- ing reason for this sentiment. We present a system for extracting supporting sentences …...

Existing concept-color-emotion lexicons limit themselves to small sets of basic emo- tions and colors, which cannot capture the rich pallet of color terms that humans use in communication. In this paper we begin to address this problem by building a novel, color-emotion-concept association lexicon via crowdsourcing. This lexicon, which we …...

In this paper we investigate the relevance of aspectual type for the problem of temporal information processing, i.e. the problems of the recent TempEval challenges. For a large list of verbs, we obtain sev- eral indicators about their lexical aspect by querying the web for expressions where these verbs occur …...

Translation models used for statistical ma- chine translation are compiled from par- allel corpora; such corpora are manually translated, but the direction of translation is usually unknown, and is consequently ig- nored. However, much research in Trans- lation Studies indicates that the direction of translation matters, as translated language (translationese) …...

Translation needs have greatly increased during the last years. In many situa- tions, text to be translated constitutes an unbounded stream of data that grows con- tinually with time. An effective approach to translate text documents is to follow an interactive-predictive paradigm in which both the system is guided by …...

This paper presents an incremental prob- abilistic learner that models the acquis- tion of syntax and semantics from a cor- pus of child-directed utterances paired with possible representations of their meanings. These meaning representations approxi- mate the contextual input available to the child; they do not specify the meanings of …...

In this paper, we present a supervised learning approach to training submodu- lar scoring functions for extractive multi- document summarization. By taking a structured prediction approach, we pro- vide a large-margin method that directly optimizes a convex relaxation of the de- sired performance measure. The learning method applies to all …...

Update summarization is a new challenge in multi-document summarization focusing on summarizing a set of recent documents relatively to another set of earlier docu- ments. We present an unsupervised proba- bilistic approach to model novelty in a doc- ument collection and apply it to the genera- tion of update summaries. …...

Topic models have great potential for help- ing users understand document corpora. This potential is stymied by their purely un- supervised nature, which often leads to top- ics that are neither entirely meaningful nor effective in extrinsic tasks (Chang et al., 2009). We propose a simple and effective way to …...