This is contrary to our original expectation that exp data which has been manually annotated for discourse connective disambiguation should outperform BLLIP which contains a lot of noise.

This finding indicates that under the multitask learning, it may not be worthy of using manually annotated corpus to generate auxiliary data.

This is because the former data is manually annotated whether a word serves as discourse connective or not, while the latter does not manually disambiguate two types of ambiguity, i.e., whether a word serves as discourse connective or not, and the type of discourse relation if it is a discourse connective.

To overcome the shortage of manually annotated training data, (Marcu and Echihabi, 2002) proposed a pattern-based approach to automatically generate training data from raw corpora.

Later, with the release of manually annotated corpus, such as Penn Discourse Treebank 2.0 (PDTB) (Prasad et al., 2008), recent studies performed implicit discourse relation recognition on natural (i.e., genuine) implicit discourse data (Pitler et al., 2009) (Lin et al., 2009) (Wang et al., 2010) with the use of linguistically informed features and machine learning algorithms.

2002) and (Sporleder and Lascarides, 2008), and (2) manually annotated explicit data with the removal of explicit discourse connectives.

Among them, it is manually annotated as an Expansion relation for 2, 938 times.

If a model only gives good performance with well-tuned hyper-parameters, then we must have manually annotated data for tuning, which would significantly reduce the applicability and portability of this method to other language pairs and tasks.

Our method does not require any manual annotation of word alignments or named entities over the bilingual training data.

On the other hand, the CRF—based NER models are trained on manually annotated data, and admit richer sequence and lexical features.

proach is that training such a feature-rich model requires manually annotated bilingual NER data, which can be prohibitively expensive to generate.

The model demonstrates performance improvements in both parsing and alignment, but shares the common limitations of other supervised work in that it requires manually annotated bilingual joint parsing and word alignment data.

It allows one to quickly construct an SRL model for a new language without manual annotation or language-specific heuristics, provided an accurate model is available for one of the related languages along with a certain amount of parallel data for the two languages.

For English-French, the English CoNLL-ST dataset was used as a source and the model was evaluated on the manually annotated dataset from van der Plas et al.

Note that in case of French we evaluate against the output of a supervised system, since manual annotation is not available for this dataset.

Several approaches have been proposed to obtain an SRL model for a new language with little or no manual annotation .

That is, it can be applied where there is no available manual annotations to train.

Instead, the supervised approach would need a large amount of manual annotations for every predicate to be processed.

However, current automatic systems require large amounts of manually annotated training data for each predicate.

The effort required for this manual annotation explains the absence of generally applicable tools.

Manually annotated corpora, such as the Chinese Treebank (CTB) (Xue et al., 2005), usually have words as the basic syntactic elements

We manually annotate the structures of 37,382 words, which cover the entire CTB5.

Luo (2003) exploited this advantage by adding flat word structures without manually annotation to CTB trees, and building a generative character-based parser.

In addition, instead of using flat structures, we manually annotate hierarchal tree structures of Chinese words for converting word-based constituent trees into character-based constituent trees.

To investigate the effects of using an in domain language model, we created a corpus composed of the manual annotations of all the documents in the Old Bailey proceedings, excluding those used in our test set.

We manually annotated the ink blotches (shown in red), and made them unobserved in the model.

We performed error analysis on our development set by randomly choosing 100 word errors from the WER alignment and manually annotating them with relevant features.

Given a large, community-authored, question-paraphrase corpus, we demonstrate that it is possible to learn a semantic lexicon and linear ranking function without manually annotating questions.

These algorithms require no manual annotation and can be applied to large, noisy databases of relational triples.

The final result is a scalable learning algorithm that requires no manual annotation of questions.

teas) independently on a manually annotated gold standard.

In Tonelli and Pighin (2009), they use these features to train an SVM-classifier to identify valid alignments and report an Fl-score of 0.66 on a manually annotated gold standard.

We compare FNWKde to two German frame-semantic resources, the manually annotated SALSA corpus (Burchardt et al., 2006) and a resource from Pado and Lapata (2005), henceforth P&L05.

The first is traditional manual annotation , as previously used by Helmreich et al.

2.2 Manual Annotation

Such a reduction may ultimately be necessary also in the case of dependency relations, but since most of our data sets were created through manual annotation , we could afford to retain a fine-grained analysis, knowing that it is always possible to map from finer to coarser distinctions, but not vice versa.4

We manually annotated each video with several sentences that describe what occurs in that video.

To evaluate our results, we manually annotated the correctness of each such pair.

The scores are thresholded to decide hits, which together with the manual annotations , can generate TP, TN, FF, and FN counts.