Index of papers in Proc. ACL 2014 that mention
  • CRFs
Feng, Vanessa Wei and Hirst, Graeme
Abstract
Our model adopts a greedy bottom-up approach, with two linear-chain CRFs applied in cascade as local classifiers.
Bottom-up tree-building
4.1 Linear-chain CRFs as Local models
Bottom-up tree-building
While our bottom-up tree-building shares the greedy framework with HILDA, unlike HILDA, our local models are implemented using CRFs .
Bottom-up tree-building
Therefore, our model incorporates the strengths of both HILDA and Joty et al.’s model, i.e., the efficiency of a greedy parsing algorithm, and the ability to incorporate sequential information with CRFs .
Experiments
For local models, our structure models are trained using MALLET (McCallum, 2002) to include constraints over transitions between adjacent labels, and our relation models are trained using CRFSuite (Okazaki, 2007), which is a fast implementation of linear-chain CRFs .
Introduction
DCRF (Dynamic Conditional Random Fields) is a generalization of linear-chain CRFs , in which each time slice contains a set of state variables and edges (Sutton et al., 2007).
Introduction
Second, by using two linear-chain CRFs to label a sequence of discourse constituents, we can incorporate contextual information in a more natural way, compared to using traditional discriminative classifiers, such as SVMs.
Post-editing
is almost identical to their counterparts of the bottom-up tree-building, except that the linear-chain CRFs in post-editing includes additional features to represent information from constituents on higher levels (to be introduced in Section 7).
CRFs is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Zeng, Xiaodong and Chao, Lidia S. and Wong, Derek F. and Trancoso, Isabel and Tian, Liang
Abstract
We propose dealing with the induced word boundaries as soft constraints to bias the continuous learning of a supervised CRFs model, trained by the treebank data (labeled), on the bilingual data (unlabeled).
Introduction
Crucially, the GP expression with the bilingual knowledge is then used as side information to regularize a CRFs (conditional random fields) model’s learning over treebank and bitext data, based on the posterior regularization (PR) framework (Ganchev et al., 2010).
Introduction
This constrained learning amounts to a jointly coupling of GP and CRFs , i.e., integrating GP into the estimation of a parametric structural model.
Methodology
language “Di and “Di: Ensure: 6: the CRFs model parameters : DCHf <— char_align_bitext (133,135) 7“ <— learn_word_bound (“Ba—’10) : Q <— encode_graph_constraint ( $133, 7“) : 6 <— pr_crf_graph (13$?wa Q)
Methodology
The GP expression will be defined as a PR constraint in Section 3.3 that reflects the interactions between the graph and the CRFs model.
Methodology
Supervised linear-chain CRFs can be modeled in a standard conditional log-likelihood objective with a Gaussian prior:
Related Work
(2008) enhanced a CRFs segmentation model in MT tasks by tuning the word granularity and improving the segmentation consistence.
Related Work
Rather than playing the “hard” uses of the bilingual segmentation knowledge, i.e., directly merging “char-to-word” alignments to words as supervisions, this study extracts word boundary information of characters from the alignments as soft constraints to regularize a CRFs model’s learning.
Related Work
(2014), proposed GP for inferring the label information of unlabeled data, and then leverage these GP outcomes to learn a semi-supervised scalable model (e. g., CRFs ).
CRFs is mentioned in 30 sentences in this paper.
Topics mentioned in this paper:
Anzaroot, Sam and Passos, Alexandre and Belanger, David and McCallum, Andrew
Background
For this underlying model, we employ a chain-structured conditional random field (CRF), since CRFs have been shown to perform better than other simple unconstrained models like hidden markov models for citation extraction (Peng and McCallum, 2004).
Background
The algorithms we present in later sections for handling soft global constraints and for learning the penalties of these constraints can be applied to general structured linear models, not just CRFs , provided we have an available algorithm for performing MAP inference.
Citation Extraction Data
Later, CRFs were shown to perform better on CORA, improving the results from the Hmm’s token-level F1 of 86.6 to 91.5 with a CRF(Peng and McCallum, 2004).
Citation Extraction Data
This approach is limited in its use of an HMM as an underlying model, as it has been shown that CRFs perform significantly better, achieving 95.37 token-level accuracy on CORA (Peng and McCallum, 2004).
Introduction
Hidden Markov models and linear-chain conditional random fields ( CRFs ) have previously been applied to citation extraction (Hetzner, 2008; Peng and McCallum, 2004) .
CRFs is mentioned in 5 sentences in this paper.
Topics mentioned in this paper: