Articles

Recent work has shown that neural- embedded word representations capture many relational similarities, which can be recovered by means of vector arithmetic in the embedded space. We show that Mikolov et al.’s method of first adding and subtracting word vectors, and then searching for a word similar to the re- …...

We demonstrate that “hallucinating” phrasal translations can significantly im- prove the quality of machine translation in low resource conditions. Our hallucinated phrase tables consist of entries composed from multiple unigram translations drawn from the baseline phrase table and from translations that are induced from mono- lingual corpora. The hallucinated phrase …...

Phrase-based translation models usually memorize local translation literally and make independent assumption between phrases which makes it neither generalize well on unseen data nor model sentence- level effects between phrases. In this pa- per we present a new method to model correlations between phrases as a Markov model and meanwhile …...

We present a Bayesian formulation for weakly-supervised learning of a Combina- tory Categorial Grammar (CCG) supertag- ger with an HMM. We assume supervi- sion in the form of a tag dictionary, and our prior encourages the use of cross- linguistically common category structures as well as transitions between tags that …...

Cross-lingual learning has become a popu- lar approach to facilitate the development of resources and tools for low-density lan- guages. Its underlying idea is to make use of existing tools and annotations in resource-rich languages to create similar tools and resources for resource-poor lan- guages. Typically, this is achieved by …...

This paper proposes to learn language- independent word representations to ad- dress cross-lingual dependency parsing, which aims to predict the dependency parsing trees for sentences in the target language by training a dependency parser with labeled sentences from a source lan- guage. We first combine all sentences from both languages …...

Bootstrapped pattern learning for entity extraction usually starts with seed entities and iteratively learns patterns and entities from unlabeled text. Patterns are scored by their ability to extract more positive en- tities and less negative entities. A prob- lem is that due to the lack of labeled data, unlabeled entities …...

Open IE methods extract structured propo- sitions from text. However, these propo- sitions are neither consolidated nor gen- eralized, and querying them may lead to insufficient or redundant information. This work suggests an approach to or- ganize open IE propositions using entail- ment graphs. The entailment relation uni- fies equivalent …...

Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byprod- uct generate highly informative vector representations for words, known as word embeddings. In this paper we present two …...

The task of detecting and generating hy- ponyms is at the core of semantic under- standing of language, and has numerous practical applications. We investigate how neural network embeddings perform on this task, compared to dependency-based vector space models, and evaluate a range of similarity measures on hyponym gener- ation. …...

Induction of common sense knowledge about prototypical sequence of events has recently received much attention (e.g., Chambers and Jurafsky (2008); Regneri et al. (2010)). Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event real- izations …...

We present a model for generating path- valued interpretations of natural language text. Our model encodes a map from natural language descriptions to paths, mediated by segmentation variables which break the language into a discrete set of events, and alignment variables which reorder those events. Within an event, lexical weights …...

In this paper, we study the impact of rela- tional and syntactic representations for an interesting and challenging task: the au- tomatic resolution of crossword puzzles. Automatic solvers are typically based on two answer retrieval modules: (i) a web search engine, e.g., Google, Bing, etc. and (ii) a database (DB) …...

Most previous work in information extraction from text has focused on named-entity recognition, entity linking, and relation extraction. Less attention has been paid given to extracting the temporal scope for relations between named entities; for example, the relation president-Of(John F. Kennedy, USA) is true only in the time-frame (January 20, …...

Linguists and psychologists have long been studying cross-linguistic transfer, the influence of native language properties on linguistic performance in a foreign lan- guage. In this work we provide empirical evidence for this process in the form of a strong correlation between language simi- larities derived from structural features in English …...

In this paper, we address the problem of converting Dialectal Arabic (DA) text that is written in the Latin script (called Arabizi) into Arabic script following the CODA convention for DA orthography. The presented system uses a finite state transducer trained at the character level to generate all possible transliterations …...

We present a data-driven framework for image caption generation which incorpo- rates visual and textual features with vary- ing degrees of spatial structure. We pro- pose the task of domain-specific image captioning, where many relevant visual details cannot be captured by off-the-shelf general-domain entity detectors. We ex- tract previously-written descriptions …...

In NLP, we need to document that our pro- posed methods perform significantly bet- ter with respect to standard metrics than previous approaches, typically by re- porting p-values obtained by rank- or randomization-based tests. We show that significance results following current re- search standards are unreliable and, in ad- dition, …...

This paper describes an approach for au- tomatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Cor- relation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phrases using a small …...

Understanding the actionable outcomes of a dialogue requires effectively modeling situational roles of dialogue participants, the structure of the dialogue and the rele- vance of each utterance to an eventual ac- tion. We develop a latent-variable model that can capture these notions and apply it in the context of courtroom …...