TRAINING CONDITIONAL RANDOM FIELDS WITH MULTIVARIATE EVALUATION MEASURES

chunking, an intermediate step towards full parsing, consists of dividing a text into syntactically correlated parts of words. The training set consists of 8936 sen- tences, each word annotated automatically with part- of-speech (POS) tags. The task is to label each word with a[r]

sion of the best sequence of labels is made after the complete analysis of an input sequence.
CRFs [3] is a rather modern approach that has al- ready become very popular for a great amount of NLP tasks due to its remarkable characteristics [9, 4, 8]. CRFs are indirected graphical models which be[r]

ture of the model. In the above example we could collapse states 1 and 4, and delay the branching until we get a dis- criminating observation. This operation is a special case of determinization (Mohri, 1997), but determinization of weighted finite-state machines is not always possible, and even w[r]

Pereira “SHALLOW PARSING WITH CONDITIONAL RANDOM FIELDS”, _Proceedings _ _of _ TRANG 16 PHỤ LỤC 1: PHƯƠNG PHÁP XÂY DỰNG DỮ LIỆU GÁN NHÃN TỪ LOẠI CHO CỤM DANH TỪ NP Ví dụ về một câu trong[r]

First, the experimental design used above has an issue shared by many CELEX-based tagging or transduction evaluations: words are randomly divided into training and test sets without be- ing grouped by stem. This means that a method can get credit for hyphenating “accents” correctly, when “acc[r]

4 Generalized Expectation Criteria for Conditional Random Fields
Prior semi-supervised learning methods have aug- mented a limited amount of fully labeled data with either unlabeled data or with constraints (e.g. fea- tures marked with their majority label[r]

To obtain a new semi-supervised training algo- rithm for CRFs, we extend the minimum entropy regularization framework of Grandvalet and Ben- gio (2004) to structured predictors. The result- ing objective combines the likelihood of the CRF on labeled training data with its con[r]

The training method described in this work is theoretically attractive, as it addresses the goal of empirical risk minimization in a very direct way. In addition to its theoretical appeal, we have shown that it performs much better than maximum likelihood and maximum pointwise likelihood t[r]

4 Miscellaneous topics on linear CRFs
4.1 Scaling CRFs for medium-sized label sets
In the previous sections we had seen that computing any important statistic like the forward-backward vectors, probability of the best labeling, or any marginal probability requires time proportional to O ( |Y| 2[r]

).
In order to reduce the amount of manual work, the precision values for each AM are based on a 10% random sample from the 10 000 highest ranked candidates. We have applied the statisti- cal test described above to obtain confidence in- tervals for the true precision values of t[r]

The patches x i,j in each image are obtained using the SIFT detector [4]. Each patch x i,j
is then represented by a feature vector φ(x i,j ) that incorporates a combination of SIFT and relative location and scale features.
The tree E is formed by running a minimum spanning tree algorithm over th[r]

TRANG 1 MARKOV RANDOM FIELDS IN IMAGE SEGMENTATION Zoltan Kato Image Processing & Computer Graphics Dept.. Extract features from the input image Each pixel _s_ in the image has a feature[r]

In this work, we study the relationship between scalability type, content type, and bitrate based on the assumption that
a single scalability choice may not fit the entire video content well [ 4 , 6 ]. We define an objective function based on specific visual distortion measures, whose weigh[r]

To determine the association of DHEA-S with exercise-training adaptation, the effect of 4 months of exercise training on insulin resistance measures was determined in a group of oldest-o[r]