Articles

Translation models often fail to generate good translations for infrequent words or phrases. Previous work attacked this problem by in- ducing new translation rules from monolin- gual data with a semi-supervised algorithm. However, this approach does not scale very well since it is very computationally expen- sive to generate new …...

This work presents a flexible and efficient discriminative training approach for statisti- cal machine translation. We propose to use the RPROP algorithm for optimizing a max- imum expected BLEU objective and experi- mentally compare it to several other updat- ing schemes. It proves to be more effi- cient and effective …...

Scenes Luis Gilberto Mateos Ortiz, Clemens Wolff and Mirella Lapata School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB {clemens.wolff,luismattor}@gmail.com, mlap@inf.ed.ac.uk Abstract Given a (static) scene, a human can effort- lessly describe what is going on (who is do- ing what to whom, how, and why). The …...

Solving the visual symbol grounding prob- lem has long been a goal of artificial intel- ligence. The field appears to be advancing closer to this goal with recent breakthroughs in deep learning for natural language ground- ing in static images. In this paper, we propose to translate videos directly to …...

Cross-lingual text classification is a major challenge in natural language processing, since often training data is available in only one language (target language), but not avail- able for the language of the document we want to classify (source language). Here, we pro- pose a method that only requires a bilingual …...

The subconscious influence of framing on per- ceptions of political issues is well-document in political science and communication re- search. A related line of work suggests that drawing attention to framing may help reduce such framing effects by enabling frame reflec- tion, critical examination of the framing un- derlying an …...

Our research aims to extract information about medication use from veterinary discussion fo- rums. We introduce the task of categoriz- ing information about medication use to deter- mine whether a doctor has prescribed medica- tion, changed protocols, observed effects, or stopped use of a medication. First, we create a medication …...

This paper presents CROWN, an automatically con- structed extension of WordNet that augments its taxonomy with novel lemmas from Wiktionary. CROWN fills the important gap in WordNet’s lexi- con for slang, technical, and rare lemmas, and more than doubles its current size. In two evaluations, we demonstrate that the construction …...

We present a data-driven technique for acquir- ing domain-level importance of verbs from the analysis of abstract/article pairs of world news articles. We show that existing lexical re- sources capture some the semantic character- istics for important words in the domain. We develop a novel characterization of the associ- ation …...

Lemmatization for the Sumerian language, compared to the modern languages, is much more challenging due to that it is a long dead language, highly skilled language experts are extremely scarce and more and more Sume- rian texts are coming out. This paper describes how our unsupervised Sumerian named-entity recognition (NER) …...

Random walks over large knowledge bases like WordNet have been successfully used in word similarity, relatedness and disambigua- tion tasks. Unfortunately, those algorithms are relatively slow for large repositories, with significant memory footprints. In this pa- per we present a novel algorithm which en- codes the structure of a knowledge …...

We present a framework for using continuous- space vector representations of word meaning to derive new vectors representing the mean- ing of senses listed in a semantic network. It is a post-processing approach that can be ap- plied to several types of word vector represen- tations. It uses two ideas: …...

Speech transcripts often only capture seman- tic content, omitting disfluencies that can be useful for analyzing social dynamics of a dis- cussion. This work describes steps in build- ing a model that can recover a large fraction of locations where disfluencies were present, by transforming carefully annotated text to match …...

A common approach to dependency parsing is scoring a parse via a linear function of a set of indicator features. These features are typically manually constructed from templates that are applied to parts of the parse tree. The templates define which properties of a part should combine to create features. …...

Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation. We introduce three new techniques to tackle these problems. First, we design the first linear-time incremental shift-reduce-style semantic parsing al- gorithm which is more efficient than conventional cubic-time bottom-up semantic …...

This paper describes a simple and princi- pled approach to automatically construct sen- timent lexicons using distant supervision. We induce the sentiment association scores for the lexicon items from a model trained on a weakly supervised corpora. Our empiri- cal findings show that features extracted from such a machine-learned lexicon …...

We introduce a simple wrapper method that uses off-the-shelf word embedding algorithms to learn task-specific bilingual word em- beddings. We use a small dictionary of easily-obtainable task-specific word equiva- lence classes to produce mixed context-target pairs that we use to train off-the-shelf em- bedding models. Our model has the advan- …...

We present a new context-aware method for lexical simplification that uses two free lan- guage resources and real web frequencies. We compare it with the state-of-the-art method for lexical simplification in Spanish and the es- tablished simplification baseline, that is, the most frequent synonym. Our method im- proves upon the …...

This work, concerning paraphrase identifica- tion task, on one hand contributes to expand- ing deep learning embeddings to include con- tinuous and discontinuous linguistic phrases. On the other hand, it comes up with a new scheme TF-KLD-KNN to learn the discrimi- native weights of words and phrases specific to paraphrase …...

Compositional embedding models build a rep- resentation for a linguistic structure based on its component word embeddings. While re- cent work has combined these word embed- dings with hand crafted features for improved performance, it was restricted to a small num- ber of features due to model complexity, thus limiting …...