Subsequently, the feature extraction stage (a VSM or a MaxEnt model as the case may be) generates the syntactic complexity feature which is then incorporated in a multiple linear regression model to generate a score.

We used the maximum entropy classifier implementation in the MaxEnt toolkit4.

The results that follow are based on MaxEnt classifier’s parameter settings initialized to zero.

The inductive classifier we use here is the maximum-entropy model ( MaxEnt ) which has been used to solve several statistical natural language processing problems with much success (Berger et al., 1996; Borthwick et al., 1998; Borthwick, 1999; Pang et al., 2002; Klein et al., 2003; Rosenfeld, 2005).

The productive feature engineering aspects of incorporating features into the discriminative MaxEnt classifier motivate the model choice for the problem at hand.

In particular, the ability of the MaxEnt model’s estimation routine to handle overlapping (correlated) features makes it directly applicable to address the first limitation of the VSM model.

MaxEnt classifiers

Once we get sense clusters for word tokens in test sentences, we load pre-trained MaXEnt classifiers of the corresponding word types.

We trained our MaxEnt classifiers with the off-the-shelf MaxEnt tool.4 We performed 100 iterations of the L-BFGS algorithm implemented in the training toolkit on the collected training events from the sense-annotated data as described in Section 3.2.

It took an average of 57.5 seconds for training a Maxent classifier.

entropy ( MaxEnt ) based classifier that is used to predict the translation probability p(é|C(c)).

The MaxEnt classifier can be formulated as follows.

This is not a issue for the MaxEnt classifier as it can deal with arbitrary overlapping features (Berger et al., 1996).

Furthermore, our framework is scalable for other local sentence level extractors in addition to the MaxEnt model.

Our ILP model and its variants all outperform Mintz++ in precision in both datasets, indicating that our approach helps filter out incorrect predictions from the output of MaxEnt model.

However, in the Riedel’s dataset, Mintz++, the MaxEnt relation extractor, does not perform well, and our framework cannot improve its performance.

Hence, our framework does not perform well due to the poor performance of MaXEnt extractor and the lack of clues.

By adopting ILP, we can combine the local information including MaXEnt confidence scores and the implicit relation backgrounds that are embedded into global consistencies of the entity tuples together.

To establish performance for the phonological standard, we use the IBPOT learner to find constraint weights but do not update M. The resultant learner is essentially MaxEnt OT with the weights estimated through Metropolis sampling instead of gradient ascent.

We consider this question by examining the dominant framework in modern phonology, Optimality Theory (Prince and Smolensky, 1993, OT), implemented in a log-linear framework, MaXEnt OT (Goldwater and Johnson, 2003), with output forms’ probabilities based on a weighted sum of

In IBPOT, we use the log-linear EVAL developed by Goldwater and J ohn-son (2003) in their MaxEnt OT system.

MEOT also is motivated by the general MaxEnt framework, whereas most other OT formulations are ad hoc constructions specific to phonology.

In MaXEnt OT, each constraint has a weight, and the candidates’ scores are the sums of the weights of violated constraints.

ment (HMM (Vogel et al., 1996) and MaxEnt (Ittycheriah and Roukos, 2005) alignment models, phrase pair extraction, MT model training (Ittycheriah and Roukos, 2007) and LM model training.

Target part-of-speech and null dependency link are exploited in a MaXEnt classifier to improve the MT quality estimation (Xiong et al., 2010).

0 17 decoding features, including phrase translation probabilities (source-to-target and target-to-source), word translation probabilities (also in both directions), maxent prob-abilitiesl, word count, phrase count, distor-

1The maxent probability is the translation probability

It shows that 1) as expected, our classifiers do worse on the harder semantic reordering prediction than syntactic reordering prediction; 2) thanks to the high accuracy obtained by the maxent classifiers, integrating either the syntactic or the semantic reordering constraints results in better reordering performance from both syntactic and semantic perspectives; 3) in terms of the mutual impact, the syntactic reordering models help improving semantic reordering more than the semantic reordering

Syntactic Semantic l-m rm l-m rm MR08 75.0 78.0 66.3 68.5 +syn-reorder 78.4 80.9 69.0 70.2 +sem—reorder 76.0 78.8 70.7 72.7 +b0th 78.6 81.7 70.6 72.1 Maxent Classifier 80.7 85.6 70.9 73.5

tactic parsing and semantic role labeling on the Chinese sentences, then train the models by using MaxEnt toolkit with L1 regularizer (Tsuruoka et al., 2009).3 Table 3 shows the reordering type distribution over the training data.

Marton and Resnik (2008) employed soft syntactic constraints with weighted binary features and no MaXEnt model.

—l— Guided DS Semi—MIML —.— DS+upsampling —'— MaxEnt

Our baselines: 1) MaXEnt is a supervised maximum entropy baseline trained on a human-labeled data; 2) DS+upsamp|ing is an upsampling experiment, where MIML was trained on a mix of a distantly-labeled and human-labeled data; 3) Semi-MIML is a recent semi-supervised extension.

We experimentally tested alternative feature sets by building supervised Maximum Entropy ( MaxEnt ) models using the hand-labeled data (Table 3), and selected an effective combination of three features from the full feature set used by Surdeanu et al., (2011):

Table 3: Performance of a MaxEnt , trained on hand-labeled data using all features (Surdeanu et al., 2011) vs using a subset of two (types of entities, dependency path), or three (adding a span word) features, and evaluated on the test set.

We use three sentiment classification techniques: Na‘1've Bayes, MaxEnt and SVM with un-igrams, bigrams and trigrams as features.

MaxEnt (Movie) -0.29 (72.17) MaxEnt (Twitter) -0.26 (71.68) SVM (Movie) -().24 (66.27) SVM (Twitter) -().19 (73.15)

MaxEnt has the highest negative correlation of -().29 and -().26.

unigram, bigram, trigram 92.6 MAXENT POS, chunks, NE, supertags

unigram, bigram, trigram 93.6 MAxENT POS, wh-word, head word

SVM 81.6 BINB 82.7 MAXENT 83.0 MAX-TDNN 78.8 NBOW 80.9 DCNN 87.4