Articles

Current approaches to cross-lingual informa- tion retrieval (CLIR) rely on standard retrieval models into which query translations by sta- tistical machine translation (SMT) are inte- grated at varying degree. In this paper, we present an attempt to turn this situation on its head: Instead of the retrieval aspect, we em- …...

Extensive lexical knowledge is necessary for temporal analysis and planning tasks. We ad- dress in this paper a lexical setting that al- lows for the straightforward incorporation of rich features and structural constraints. We ex- plore a lexical event ordering task, namely de- termining the likely temporal order of events …...

This paper introduces a tensor-based approach to semantic role labeling (SRL). The motiva- tion behind the approach is to automatically induce a compact feature representation for words and their relations, tailoring them to the task. In this sense, our dimensionality reduction method provides a clear alternative to the traditional feature …...

This paper describes IDEST, a new method for learning paraphrases of event patterns. It is based on a new neural network architecture that only relies on the weak supervision sig- nal that comes from the news published on the same day and mention the same real-world en- tities. It can …...

Meaning Representation Xiaoman Pan1, Taylor Cassidy2, Ulf Hermjakob3, Heng Ji1, Kevin Knight3 1Computer Science Department, Rensselaer Polytechnic Institute {panx2,jih}@rpi.edu 2IBM Research & Army Research Laboratory taylor.cassidy.civ@mail.mil 3Information Sciences Institute, University of Southern California {ulf,knight}@isi.edu Abstract Most successful Entity Linking (EL) meth- ods aim to link mentions to their referent en- …...

Coreference is a core nlp problem. How- ever, newswire data, the primary source of existing coreference data, lack the rich- ness necessary to truly solve coreference. We present a new domain with denser references—quiz bowl questions—that is challenging and enjoyable to humans, and we use the quiz bowl community to …...

Matrix factorization approaches to relation extraction provide several attractive features: they support distant supervision, handle open schemas, and leverage unlabeled data. Unfortu- nately, these methods share a shortcoming with all other distantly supervised approaches: they cannot learn to extract target relations with- out existing data in the knowledge base, and …...

Previous work on text coherence was primar- ily based on matching multiple mentions of the same entity in different parts of the text; therefore, it misses the contribution from se- mantically related but not necessarily coref- erential entities (e.g., Gates and Microsoft). In this paper, we capture such semantic relat- …...

ive Summarization Using Semantic Representations Fei Liu Jeffrey Flanigan Sam Thomson Norman Sadeh Noah A. Smith School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA {feiliu, jflanigan, sthomson, sadeh, nasmith}@cs.cmu.edu Abstract We present a novel abstractive summarization framework that draws on the recent develop- ment of a treebank …...

In this paper we study the task of movie script summarization, which we argue could enhance script browsing, give readers a rough idea of the script’s plotline, and speed up read- ing time. We formalize the process of gen- erating a shorter version of a screenplay as the task of …...

Existing timeline generation systems for com- plex events consider only information from traditional media, ignoring the rich social con- text provided by user-generated content that reveals representative public interests or in- sightful opinions. We instead aim to gen- erate socially-informed timelines that con- tain both news article summaries and selected …...

A major opportunity for NLP to have a real- world impact is in helping educators score stu- dent writing, particularly content-based writ- ing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular ques- tion (i.e., labeled data) are …...

Transcribing documents from the printing press era, a challenge in its own right, is more complicated when documents interleave multiple languages—a common feature of 16th century texts. Additionally, many of these documents precede consistent ortho- graphic conventions, making the task even harder. We extend the state-of-the-art his- torical OCR model …...

Citation sentences (citances) to a reference ar- ticle have been extensively studied for sum- marization tasks. However, citances might not accurately represent the content of the cited article, as they often fail to capture the con- text of the reported findings and can be af- fected by epistemic value drift. …...

Recent work has successfully leveraged the semantic information extracted from lexi- cal knowledge bases such as WordNet and FrameNet to improve English event corefer- ence resolvers. The lack of comparable re- sources in other languages, however, has made the design of high-performance non-English event coreference resolvers, particularly those employing unsupervised …...

We present the first dynamic programming (DP) algorithm for shift-reduce constituency parsing, which extends the DP idea of Huang and Sagae (2010) to context-free grammars. To alleviate the propagation of errors from part-of-speech tagging, we also extend the parser to take a tag lattice instead of a fixed tag sequence. …...

Supervised morphological paradigm learning by identifying and aligning the longest com- mon subsequence found in inflection tables has recently been proposed as a simple yet competitive way to induce morphological pat- terns. We combine this non-probabilistic strat- egy of inflection table generalization with a discriminative classifier to permit the recon- …...

We present APRO, a new method for machine translation tuning that can handle large feature sets. As opposed to other popular methods (e.g., MERT, MIRA, PRO), which involve ran- domness and require multiple runs to obtain a reliable result, APRO gives the same result on any run, given initial feature …...

We propose the use of neural networks to model source-side preordering for faster and better statistical machine translation. The neu- ral network trains a logistic regression model to predict whether two sibling nodes of the source-side parse tree should be swapped in order to obtain a more monotonic parallel corpus, …...

Word embedding has been found to be high- ly powerful to translate words from one lan- guage to another by a simple linear transfor- m. However, we found some inconsistence among the objective functions of the embed- ding and the transform learning, as well as the distance measurement. This paper …...