Articles

Transitional expressions provide glue that holds ideas together in a text and enhance the logical organization, which together help im- prove readability of a text. However, in most current statistical machine translation (SMT) systems, the outputs of compound-complex sentences still lack proper transitional expres- sions. As a result, the translations …...

The essence of distantly supervised rela- tion extraction is that it is an incomplete multi-label classification problem with s- parse and noisy features. To tackle the s- parsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification …...

In this paper, we present a manifold model for medical relation extraction. Our model is built upon a medical corpus containing 80M sentences (11 gigabyte text) and de- signed to accurately and efficiently detect the key medical relations that can facilitate clinical decision making. Our approach integrates domain specific parsing …...

Most existing relation extraction models make predictions for each entity pair lo- cally and individually, while ignoring im- plicit global clues available in the knowl- edge base, sometimes leading to conflicts among local predictions from different en- tity pairs. In this paper, we propose a joint inference framework that utilizes …...

This paper studies the idea of remov- ing low-frequency words from a corpus, which is a common practice to reduce computational costs, from a theoretical standpoint. Based on the assumption that a corpus follows Zipf’s law, we derive trade- off formulae of the perplexity of k-gram models and topic models …...

We introduce three linguistically moti- vated structured regularizers based on parse trees, topics, and hierarchical word clusters for text categorization. These regularizers impose linguistic bias in fea- ture weights, enabling us to incorporate prior knowledge into conventional bag- of-words models. We show that our structured regularizers consistently im- prove classification …...

Entity clustering must determine when two named-entity mentions refer to the same entity. Typical approaches use a pipeline ar- chitecture that clusters the mentions using fixed or learned measures of name and con- text similarity. In this paper, we propose a model for cross-document coreference res- olution that achieves robustness …...

Widely used in speech and language pro- cessing, Kneser-Ney (KN) smoothing has consistently been shown to be one of the best-performing smoothing methods. However, KN smoothing assumes integer counts, limiting its potential uses—for ex- ample, inside Expectation-Maximization. In this paper, we propose a generaliza- tion of KN smoothing that operates …...

This paper presents a novel framework called error case frames for correcting preposition errors. They are case frames specially designed for describing and cor- recting preposition errors. Their most dis- tinct advantage is that they can correct er- rors with feedback messages explaining why the preposition is erroneous. This pa- …...

In this paper, we investigate various strate- gies to predict both syntactic dependency parsing and contiguous multiword expres- sion (MWE) recognition, testing them on the dependency version of French Tree- bank (Abeill´e and Barrier, 2004), as in- stantiated in the SPMRL Shared Task (Seddah et al., 2013). Our work focuses …...

We propose a two-phase framework to adapt existing relation extraction classi- fiers to extract relations for new target do- mains. We address two challenges: neg- ative transfer when knowledge in source domains is used without considering the differences in relation distributions; and lack of adequate labeled samples for rarer relations …...

In this paper we address the problem of grounding distributional representations of lexical meaning. We introduce a new model which uses stacked autoencoders to learn higher-level embeddings from tex- tual and visual input. The two modali- ties are encoded as vectors of attributes and are obtained automatically from text and …...

The automatic estimation of machine translation (MT) output quality is a hard task in which the selection of the appro- priate algorithm and the most predictive features over reasonably sized training sets plays a crucial role. When moving from controlled lab evaluations to real-life sce- narios the task becomes even …...

This paper tackles the sparsity problem in estimating phrase translation probabilities by learning continuous phrase representa- tions, whose distributed nature enables the sharing of related phrases in their represen- tations. A pair of source and target phrases are projected into continuous-valued vec- tor representations in a low-dimensional latent space, where …...

We present experiments in using dis- course structure for improving machine translation evaluation. We first design two discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory. Then, we show that these measures can help improve a number of existing machine …...

We propose an online learning algorithm based on tensor-space models. A tensor- space model represents data in a compact way, and via rank-1 approximation the weight tensor can be made highly struc- tured, resulting in a significantly smaller number of free parameters to be estimated than in comparable vector-space models. …...

We propose three improvements to ad- dress the drawbacks of state-of-the-art transition-based constituent parsers. First, to resolve the error propagation problem of the traditional pipeline approach, we incorporate POS tagging into the syntac- tic parsing process. Second, to allevi- ate the negative influence of size differ- ences among competing action …...

Representing predicates in terms of their argument distribution is common practice in NLP. Multi-word predicates (MWPs) in this context are often either disregarded or considered as fixed expressions. The lat- ter treatment is unsatisfactory in two ways: (1) identifying MWPs is notoriously diffi- cult, (2) MWPs show varying degrees of …...

The ability to accurately represent sen- tences is central to language understand- ing. We describe a convolutional architec- ture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pool- ing, a global pooling operation over lin- ear sequences. …...

Statistical phrase-based translation learns translation rules from bilingual corpora, and has traditionally only used monolin- gual evidence to construct features that rescore existing translation candidates. In this work, we present a semi-supervised graph-based approach for generating new translation rules that leverages bilingual and monolingual data. The proposed tech- nique first …...