§2 introduces the maximum entropy (maxent) and conditional random field ( CRF ) learning techniques employed, along with specifications for the design and training of our hierarchical prior.

Specifically, we compared our approximate hierarchical prior model (HIER), implemented as a CRF, against three baselines: o GAUSS: CRF model tuned on a single domain’s data, using a standard N(0, 1) prior 0 CAT: CRF model tuned on a concatenation of multiple domains’ data, using a N(0, 1) prior 0 CHELBA: CRF model tuned on one domain’s data, using a prior trained on a different, related domain’s data (cf.

Line a shows the F1 performance of a CRF model tuned only on the target MUC6 domain (GAUSS) across a range of tuning data sizes.

Line I) shows the same experiment, but this time the CRF model has been tuned on a dataset comprised of a simple concatenation of the training MUC6 data from (a), along with a different training set from MUC7 (CAT).

The parametric form of the CRF for a sentence of length n is given as follows:

CRF learns a model consisting of a set of weights A = {A1...)\F} over the features so as to maximize the conditional likelihood of the training data, p(lémm|Xtmm), given the model p A.

2.2 CRF with Gaussian priors

Finally, we will briefly introduce CRF models and the features that we used for CRF model.

A CRF is an undirected graphical model G of the conditional distribution P(Y|X).

Linear CRF model has been successfully applied in NLP and text mining tasks (McCallum and Li, 2003; Sha and Pereira, 2003).

To capture the dependency between contexts and answers, we introduce Skip-chain CRF model for answer detection.

Experimental results show that 1) Linear CRFs outperform SVM and decision tree in both context and answer detection; 2) Skip-chain CRFs outperform Linear CRFs for answer finding, which demonstrates that context improves answer finding; 3) 2D CRF model improves the performance of Linear CRFs and the combination of 2D CRFs and Skip-chain CRFs achieves better performance for context detection.

Due to the sequential nature of our RE task, H-CRF employs a CRF as the meta-leamer, as opposed to a decision tree or regression-based classifier.

To obtain the probability at each position of a linear-chain CRF , the constrained forward-backward technique described in (Culotta and McCallum, 2004) is used.

(2006) used a CRF for RE, yet their task differs greatly from open extraction.

Figure 1: Relation Extraction as Sequence Labeling: A CRF is used to identify the relationship, born in, between Kafka and Prague

The resulting set of labeled examples are described using features that can be extracted without syntactic or semantic analysis and used to train a CRF , a sequence model that learns to identify spans of tokens believed to indicate explicit mentions of relationships between entities.

The entity pair serves to anchor each end of a linear-chain CRF , and both entities in the pair are assigned a fixed label of ENT.

For example, in (Lafferty et al., 2001), when switching from a generatively trained hidden Markov model (HMM) to a discriminatively trained, linear chain, conditional random field ( CRF ) for part-of-speech tagging, their error drops from 5.7% to 5.6%.

When they add in only a small set of orthographic features, their CRF error rate drops considerably more to 4.3%, and their out-of-vocabulary error rate drops by more than half.

We then define a conditional probability distribution over entire trees, using the standard CRF distribution, shown in (1).

We generated the node and the edge features of a CRF model as described in Table 3 using these atomic features.

To train CRF models, we used Taku Kudo’s CRF++ (ver.

These annotated IOB tags can be used in the same way as other features in a CRF tagger.