We are interested in parsing constituency-based grammars such as HPSG and CCG using a small amount of data specific for the target formalism, and a large quantity of coarse CFG annotations from the Penn Treebank .

While all of the target formalisms share a similar basic syntactic structure with Penn Treebank CFG, they also encode additional constraints and semantic features.

The standard solution to this bottleneck has relied on manually crafted transformation rules that map readily available syntactic annotations (e.g, the Penn Treebank ) to the desired formalism.

A natural candidate for such coarse annotations is context-free grammar (CFG) from the Penn Treebank , while the target formalism can be any constituency-based grammars, such as Combinatory Categorial Grammar (CCG) (Steedman, 2001), Lexical Functional Grammar (LFG) (Bresnan, 1982) or Head-Driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994).

All of these formalisms share a similar basic syntactic structure with Penn Treebank CFG.

For instance, mappings may specify how to convert traces and functional tags in Penn Treebank to the f-structure in LFG (Cahill, 2004).

For instance, Hockenmaier and Steedman (2002) made thousands of POS and constituent modifications to the Penn Treebank to facilitate transfer to CCG.

Unlike the context-free grammars extracted from the Penn treebank , these allow for the categorial semantics that accompanies any categorial parse and for a more elegant analysis of linguistic structures such as extraction and coordination.

in Petrov’s experiments on the Penn treebank , the syntactic category NP was refined to the more fine-grained N P1 and N P2 roughly corresponding to N Ps in subject and object positions.

In the supertagging literature, POS tagging and supertagging are distinguished — POS tags are the traditional Penn treebank tags (e. g. NN, VBZ and DT) and supertags are CCG categories.

The Petrov parser (Petrov and Klein, 2007) uses latent variables to refine the grammar extracted from a corpus to improve accuracy, originally used to improve parsing results on the Penn treebank (PTB).

These results should not be interpreted as proof that grammars extracted from the Penn treebank and from CCGbank are equivalent.

CCGbank (Hockenmaier and Steedman, 2007) is a corpus of CCG derivations that was semiautomatically converted from the Wall Street J our-nal section of the Penn treebank .

For POS/parsing annotation, there are also a number of annotation schemes including the Brown tag set, the Claws tag set, and the Penn Treebank tag set.

For instance, there are at least three possibilities for POS-tagging the word sing in the sentence everyone sing together using the Penn Treebank tag set: singN B, sing/VBP, or sing/VBZ.

For similar reasons, to the best of our knowledge, there exists no such learner corpus that is manually shallow-parsed and which is also publicly available, unlike, say, native-speaker corpora such as the Penn Treebank .

We selected the Penn Treebank tag set, which is one of the most widely used tag sets, for our

Similar to the error annotation scheme, we conducted a pilot study to determine what modifications we needed to make to the Penn Treebank scheme.

As a result of the pilot study, we found that the Penn Treebank tag set sufficed in most cases except for errors which learners made.

Both use the Penn Treebank POS tag set.

An obvious cause of mistakes in both taggers is that they inevitably make errors in the POSs that are not defined in the Penn Treebank tag set, that is, UK and CE.

For these experiments, we use the Wall Street Journal portion of the Penn Treebank (Marcus et al., 1993).

Following the CoNLL shared task from 2000, we use sections 15-18 of the Penn Treebank for our labeled training data for the supervised sequence labeler in all experiments (Tjong et al., 2000).

For the tagging experiments, we train and test using the gold standard POS tags contained in the Penn Treebank .

We devise a gold-standard sense- and parse tree-annotated dataset based on the intersection of the Penn Treebank and SemCor, and experiment with different approaches to both semantic representation and disambiguation.

Traditionally, the two parsers have been trained and evaluated over the WSJ portion of the Penn Treebank (PTB: Marcus et al.

We diverge from this norm in focusing exclusively on a sense-annotated subset of the Brown Corpus portion of the Penn Treebank , in order to investigate the upper bound performance of the models given gold-standard sense information.

most closely related research is that of Bikel (2000), who merged the Brown portion of the Penn Treebank with SemCor (similarly to our approach in Section 4.1), and used this as the basis for evaluation of a generative bilexical model for joint WSD and parsing.

As far as we know, these are the first results over both WordNet and the Penn Treebank to show that semantic processing helps parsing.

The only publicly-available resource with these two characteristics at the time of this work was the subset of the Brown Corpus that is included in both SemCor (Landes et al., 1998) and the Penn Treebank (PTB).2 This provided the basis of our dataset.

We provide the first definitive results that word sense information can enhance Penn Treebank parser performance, building on earlier results of Bikel (2000) and Xiong et al.

This is a significant problem for CCGbank, where binary branching NP derivations are often incorrect, a result of the automatic conversion from the Penn Treebank .

Recently, Vadas and Curran (2007a) annotated internal NP structure for the entire Penn Treebank , providing a large gold-standard corpus for NP bracketing.

We apply one preprocessing step on the Penn Treebank data, where if multiple tokens are enclosed by brackets, then a NML node is placed around those

Since we are applying these to CCGbank NP structures rather than the Penn Treebank , the POS tag based heuristics are sufficient to determine heads accurately.

Vadas and Curran (2007a) experienced a similar drop in performance on Penn Treebank data, and noted that the F-score for NML and JJP brackets was about 20% lower than the overall figure.

This is because their training data, the Penn Treebank (Marcus et al., 1993), does not fully annotate NP structure.

The flat structure described by the Penn Treebank can be seen in this example:

CCGbank (Hockenmaier and Steedman, 2007) is the primary English corpus for Combinatory Categorial Grammar (CCG) (Steedman, 2000) and was created by a semiautomatic conversion from the Penn Treebank .

Using the Penn Treebank sentences associated with each SR Task dependency tree, we will create the two tree sets necessary to support error mining by dividing the set of trees output by the surface realiser into a set of trees (FAIL) associated with overgeneration (the generated sentences do not match the original sentences) and a set of trees (SUCCESS) associated with success (the generated sentence matches the original sentences).

The shallow input data provided by the SR Task was obtained from the Penn Treebank using the LTH Constituent—to—Dependency Conversion Tool for Penn—style Treebanks (Pennconverter, (J ohans—son and Nugues, 2007)).

The chunking was performed by retrieving from the Penn Treebank (PTB), for each phrase type, the yields of the constituents of that type and by using the alignment between words and dependency tree nodes provided by the organisers of the SR Task.

5 In the Penn Treebank , the POS tag is the category assigned to possessive ’s.

(Callaway, 2003) avoids this shortcoming by converting the Penn Treebank to the format expected by his realiser.

We use the standard test set for this task, a 24,115-word subset of the Penn Treebank , for which a gold tag sequence is available.

They show considerable improvements in tagging accuracy when using a coarser-grained version (with l7-tags) of the tag set from the Penn Treebank .

In contrast, we keep all the original dictionary entries derived from the Penn Treebank data for our experiments.

Their models are trained on the entire Penn Treebank data (instead of using only the 24,115-token test data), and so are the tagging models used by Goldberg et al.

ing data from the 24,115-t0ken set to the entire Penn Treebank (973k tokens).

Their systems were shown to obtain considerable improvements in accuracy when using a l7-tagset (a coarser-grained version of the tag labels from the Penn Treebank ) instead of the 45-tagset.

The accuracy numbers reported for Init-HMM and LDA+AC are for models that are trained on all the available unlabeled data from the Penn Treebank .

The IP+EM models used in the l7-tagset experiments reported here were not trained on the entire Penn Treebank , but instead used a smaller section containing 77,963 tokens for estimating model parameters.

In the corpus, we manually generated parallel trees for about 5,000 sentences from Penn Treebank .

We translated and transformed a subset of parse trees of Penn Treebank to Turkish.

As a future work, we plan to expand the dataset to include all Penn Treebank sentences.

In order to constrain the syntactic complexity of the sentences in the corpus, we selected from the Penn Treebank II 9560 trees which contain a maximum of 15 tokens.

These include 8660 trees from the training set of the Penn Treebank , 360 trees from its development set and 540 trees from its test set.

MaltParser is trained on the Penn Treebank for English, on the Swedish treebank Talbanken05 (Nivre et al., 2006b), and on the METU-Sabanc1 Turkish Treebank (Atalay et al., 2003), respectively.

In the Penn Treebank II annotation, the movement leaves a trace and is associated with wh- constituent with a numeric marker.

Statistical parsers induce their grammars from corpora, and the corpora for linguistically motivated formalisms currently do not contain high quality predicate-argument annotation, because they were derived from the Penn Treebank (PTB Marcus et al., 1993).

The structure of such compound noun phrases is left underspecified in the Penn Treebank (PTB), because the annotation procedure involved stitching together partial parses produced by the Fid-ditch parser (Hindle, 1983), which produced flat brackets for these constructions.

The syntactic analysis of punctuation is notoriously difficult, and punctuation is not always treated consistently in the Penn Treebank (Bies et al., 1995).

The most cited computational linguistics work to date is the Penn Treebank (Marcus et al., l993)1.

We chose to work on CCGbank (Hockenmaier and Steedman, 2007), a Combinatory Categorial Grammar (Steedman, 2000) treebank acquired from the Penn Treebank (Marcus et al., 1993).

Our analysis requires semantic role labels for each argument of the nominal predicates in the Penn Treebank — precisely what NomBank (Meyers et al., 2004) provides.

First, we align CCGbank and the Penn Treebank , and produce a version of NomBank that refers to CCGbank nodes.

Note that all grammar rules in ERG are either unary or binary, giving us relatively deep trees when compared with annotations such as Penn Treebank .

For these rules, we refer to the conversion of the Penn Treebank into dependency structures used in the CoNLL 2008 Shared Task, and mark the heads of these rules in a way that will arrive at a compatible dependency backbone.

2More recent study shows that with carefully designed retokenization and preprocessing rules, over 80% sentential coverage can be achieved on the WSJ sections of the Penn Treebank data using the same version of ERG.

The larger part is converted from the Penn Treebank Wall Street Journal Sections #2—#21, and is used for training statistical dependency parsing models; the smaller part, which covers sentences from Section #23, is used for testing.

Brown This dataset contains a subset of converted sentences from BROWN sections of the Penn Treebank .

Although the original annotation scheme is similar to the Penn Treebank , the dependency extraction setting is slightly different to the CoNLLWSJ dependencies (e.g.

the Wall Street Journal (WSJ) sections of the Penn Treebank (Marcus et al., 1993) as training set, tests on BROWN Sections typically result in a 6-8% drop in labeled attachment scores, although the average sentence length is much shorter in BROWN than that in WSJ.

21,938 total examples, 15,330 come from sections 2—21 of the Penn Treebank (Marcus et al., 1993).

For the Penn Treebank , we extracted the examples using the provided gold standard parse trees, whereas, for the latter cases, we used the output of an open source parser (Tratz and Hovy, 2011).

The accuracy figures for the test instances from the Penn Treebank , The Jungle Book, and The History of the Decline and Fall of the Roman Empire were 88.8%, 84.7%, and 80.6%, respectively.

The NomBank project (Meyers et al., 2004) provides coarse annotations for some of the possessive constructions in the Penn Treebank , but only those that meet their criteria.

Penn Treebank , respectively.

portion of the Penn Treebank .

The Penn Treebank and The History of the Decline and Fall of the R0-man Empire were substantially more similar, although there are notable differences.

It should be noted, however, that this conversion cannot work perfectly with automatic parsing, because the conversion program relies on function tags and empty categories of the original Penn Treebank .

10Some of the parser packages include parsing models trained with extended data, but we used the models trained with WSJ section 2-21 of the Penn Treebank .

This assumes the existence of a gold-standard test corpus, such as the Penn Treebank (Marcus et al., 1994).

Most state-of-the-art parsers for English were trained with the Wall Street Journal (WSJ) portion of the Penn Treebank , and high accuracy has been reported for WSJ text; however, these parsers rely on lexical information to attain high accuracy, and it has been criticized that these parsers may overfit to WSJ text (Gildea, 2001;

In general, our evaluation methodology can be applied to English parsers based on any framework; however, in this paper, we chose parsers that were originally developed and trained with the Penn Treebank or its variants, since such parsers can be retrained with GENIA, thus allowing for us to investigate the effect of domain adaptation.

Owing largely to the Penn Treebank , the mainstream of data-driven parsing research has been dedicated to the phrase structure parsing.

ENJU The HPSG parser that consists of an HPSG grammar extracted from the Penn Treebank , and a maximum entropy model trained with an HPSG treebank derived from the Penn Treebank.7

Results on the Penn Treebank show that our conversion method achieves 42% error reduction over the previous best result.

Future work includes further investigation of our conversion method for other pairs of grammar formalisms, e.g., from the grammar formalism of the Penn Treebank to more deep linguistic formalism like CCG, HPSG, or LFG.

(2008) used WSJ section 19 from the Penn Treebank to extract DS to PS conversion rules and then produced dependency trees from WSJ section 22 for evaluation of their DS to PS conversion algorithm.

5 We used the tool “Penn2Malt” to produce dependency structures from the Penn Treebank , which was also used for PS to DS conversion in our conversion algorithm.

We have evaluated our conversion algorithm on a dependency structure treebank (produced from the Penn Treebank ) for comparison with previous work (Xia et al., 2008).

Section 3 provides experimental results of grammar formalism conversion on a dependency treebank produced from the Penn Treebank .

In Table l, we show the first four samples of length between 15 and 20 generated from our model and a 5- gram model trained on the Penn Treebank .

For training data, we constructed a large treebank by concatenating the WSJ and Brown portions of the Penn Treebank , the 50K BLLIP training sentences from Post (2011), and the AFP and APW portions of English Gigaword version 3 (Graff, 2003), totaling about 1.3 billion tokens.

We used the human-annotated parses for the sentences in the Penn Treebank , but parsed the Gigaword and BLLIP sentences with the Berkeley Parser.

Figure 2: A sample parse from the Penn Treebank after the tree transformations described in Section 3.

Although the Penn Treebank annotates temporal N Ps, most off-the-shelf parsers do not retain these tags, and we do not assume their presence.

There is one additional hurdle in the estimation of our model: while there exist corpora with human-annotated constituency parses like the Penn Treebank (Marcus et al., 1993), these corpora are quite small — on the order of millions of tokens — and we cannot gather nearly as many counts as we can for 77.-grams, for which billions or even trillions (Brants et al., 2007) of tokens are available on the Web.

Table 2: Results for the Penn Treebank development set, sentences of length g 40, for different annotation schemes implemented on top of the X-bar grammar.

Table 3: Final Parseval results for the v = l, h = 0 parser on Section 23 of the Penn Treebank .

Finally, Table 3 shows our final evaluation on Section 23 of the Penn Treebank .

Table 1 shows the results of incrementally building up our feature set on the Penn Treebank development set.

Because the X-bar grammar is so minimal, this grammar does not parse very accurately, scoring just 73 F1 on the standard English Penn Treebank task.

Throughout this and the following section, we will draw on motivating examples from the English Penn Treebank , though similar examples could be equally argued for other languages.

The most well-known multiply-annotated and validated corpus of English is the one million word Wall Street Journal corpus known as the Penn Treebank (Marcus et al., 1993), which over the years has been fully or partially annotated for several phenomena over and above the original part-of-speech tagging and phrase structure annotation.

More recently, the OntoNotes project (Pradhan et al., 2007) released a one million word English corpus of newswire, broadcast news, and broadcast conversation that is annotated for Penn Treebank syntax, PropBank predicate argument structures, coreference, and named entities.

words Token Validated 1 18 222472 Sentence Validated 1 18 222472 POS/lemma Validated 1 18 222472 Noun chunks Validated 1 18 222472 Verb chunks Validated 1 18 222472 Named entities Validated 1 18 222472 FrameNet frames Manual 21 17829 HSPG Validated 40* 30106 Discourse Manual 40* 30106 Penn Treebank Validated 97 873 83 PropB ank Validated 92 50165 Opinion Manual 97 47583 TimeB ank Validated 34 5434 Committed belief Manual 13 4614 Event Manual 13 4614 Coreference Manual 2 1 877

Annotations produced by other projects and the FrameNet and Penn Treebank annotations produced specifically for MASC are semiautomatically and/or manually produced by those projects and subjected to their internal quality controls.

All of the first 80K increment is annotated for Penn Treebank syntax.

The second 120K increment includes 5.5K words of Wall Street Journal texts that have been annotated by several projects, including Penn Treebank , PropBank, Penn Discourse Treebank, TimeML, and the Pittsburgh Opinion project.

We perform parsing experiments the Penn Treebank and draw comparisons to Tree-Substitution Grammars and between different variations in probabilistic model design.

As a proof of concept, we investigate OSTAG in the context of the classic Penn Treebank statistical parsing setup; training on section 2-21 and testing on section 23.

Furthermore, the various parameteri-zations of adjunction with OSTAG indicate that, at least in the case of the Penn Treebank , the finer grained modeling of a full table of adjunction probabilities for each Goodman index OSTAG3 overcomes the danger of sparse data estimates.

We evaluate OSTAG on the familiar task of parsing the Penn Treebank .

We propose a simple but empirically effective heuristic for grammar induction for our experiments on Penn Treebank data.

We used three corpora for experiments: WSJ from Penn Treebank , Wikipedia, and the general Web.

In contrast, TextRunner was trained with 91,687 positive examples and 96,795 negative examples generated from the WSJ dataset in Penn Treebank .

We used three parsing options on the WSJ dataset: Stanford parsing, C] 50 parsing (Charniak and Johnson, 2005), and the gold parses from the Penn Treebank .

For example, TextRunner uses a small set of handwritten rules to heuristically label training examples from sentences in the Penn Treebank .

In both cases, however, we generate training data from Wikipedia by matching sentences with infoboxes, while TextRunner used a small set of handwritten rules to label training examples from the Penn Treebank .

Articles in the Penn TreeBank were identified as being reviews, summaries, letters to the editor, news reportage, corrections, wit and short verse, or quarterly profit reports.

This paper has, for the first time, provided genre information about the articles in the Penn TreeBank .

Although the files in the Penn TreeBank (PTB) lack any classificatory meta-data, leading the PTB to be treated as a single homogeneous collection of “news articles”, researchers who have manually examined it in detail have noted that it includes a variety of “financial reports, general interest stories, business-related news, cultural reviews, editorials and letters to the editor” (Carlson et al., 2002, p. 7).

the Penn TreeBank that aren’t included in the PDTB.

This paper considers differences in texts in the well-known Penn TreeBank (hereafter, PTB) and in particular, how these differences show up in the Penn Discourse TreeBank (Prasad et al., 2008).

We demonstrate the effectiveness of the approach in a series of dependency parsing experiments on the Penn Treebank and Prague Dependency Treebank, and we show that the cluster-based features yield substantial gains in performance across a wide range of conditions.

The English experiments were performed on the Penn Treebank (Marcus et al., 1993), using a standard set of head-selection rules (Yamada and Matsumoto, 2003) to convert the phrase structure syntax of the Treebank to a dependency tree representation.6 We split the Treebank into a training set (Sections 2—21), a development set (Section 22), and several test sets (Sections 0,7 1, 23, and 24).

9We ensured that the sentences of the Penn Treebank were excluded from the text used for the clustering.

We show that our semi-supervised approach yields improvements for fixed datasets by performing parsing experiments on the Penn Treebank (Marcus et al., 1993) and Prague Dependency Treebank (Hajic, 1998; Hajic et al., 2001) (see Sections 4.1 and 4.3).

Implicit arguments have not been annotated within the Penn TreeBank , which is the textual and syntactic basis for NomBank.

Consider the following abridged sentences, which are adjacent in their Penn TreeBank document:

Starting with a wide range of features, we performed floating forward feature selection (Pudil et al., 1994) over held-out development data comprising implicit argument annotations from section 24 of the Penn TreeBank .

However, as shown by the following example from the Penn TreeBank (Marcus et al., 1993), this restriction excludes extra-sentential arguments:

It achieves 87.8% labelled attachment score and 88.8% unlabeled attachment score on the standard Penn Treebank test set.

On the standard Penn Treebank test set, it achieves an F-score of 89.5%.

The parser preprocesses the Penn Treebank training data through binarization.

For example, Penn Treebank annotations are often flat at the phrase level.

Table 1: Performance numbers for computing Viterbi inside charts on 20,000 sentences of length $40 from the Penn Treebank .

As with other grammars with a parse/derivation distinction, the grammars of Petrov and Klein (2007) only achieve their full accuracy using minimum-Bayes-risk parsing, with improvements of over 1.5 F1 over best-derivation Viterbi parsing on the Penn Treebank (Marcus et al., 1993).

Table 2: Performance numbers for computing max constituent (Goodman, 1996) trees on 20,000 sentences of length 40 or less from the Penn Treebank .

We measured parsing accuracy on sentences of length g 40 from section 22 of the Penn Treebank .

We study the effect of semantic classes in three dependency parsers, using two types of constituency-to-dependency conversions of the English Penn Treebank .

supervised approach that makes use of cluster features induced from unlabeled data, providing significant performance improvements for supervised dependency parsers on the Penn Treebank for English and the Prague Dependency Treebank for Czech.

Most experiments for English were evaluated on the Penn2Malt conversion of the constituency-based Penn Treebank .

The results showed a signi-cant improvement, giving the first results over both WordNet and the Penn Treebank (PTB) to show that semantics helps parsing.

Following this POS representation, there are as many as 10 possible POS tags that may occur in between the—0f, as estimated from the WSJ corpus of Penn Treebank .

To explore determinacy in the distribution of POS tags in Penn Treebank , we need to consider that a POS tag marks the basic syntactic category of a word as well as its morphological inflection.

Table 1: Morph features of frequent words and rare words as computed from the WSJ Corpus of Penn Treebank .

We trained our model on sections 2—21 of the WSJ part of the Penn Treebank (Marcus et al., 1999).

Unfortunately, marking for argument/modifiers in the Penn Treebank is incomplete, and is limited to certain adverbials, e.g.

This corpus adds annotations indicating, for each node in the Penn Treebank , whether that node is a modifier.

3This data sparsity problem is quite severe — for example, the Penn treebank (Marcus et a1., 1993) has a total number of 43,498 sentences, with 42,246 unique POS tag sequences, averaging to be 1.04.

For English we use the Penn treebank (Marcus et al., 1993), with sections 2—21 for training and section 23 for final testing.

For both methods we chose the best parameters for sentences of length 6 g 10 on the English Penn Treebank (training) and used this set for all other experiments.

According to our survey on the measure word distribution in the Chinese Penn Treebank and the test datasets distributed by Linguistic Data Consortium (LDC) for Chinese-to-English machine translation evaluation, the average occurrence is 0.505 and 0.319 measure

Table 1 shows the relative position’s distribution of head words around measure words in the Chinese Penn Treebank , where a negative position indicates that the head word is to the left of the measure word and a positive position indicates that the head word is to the right of the measure word.

According to our survey, about 70.4% of measure words in the Chinese Penn Treebank need

Labeled English data employed in this paper were derived from the Wall Street Journal (WSJ) corpus of the Penn Treebank (Marcus et al., 1993).

In addition, we removed from the unlabeled English data the sentences that appear in the WSJ corpus of the Penn Treebank .

On standard evaluations using both the Penn Treebank and the Penn Chinese Treebank, our parser gave higher accuracies than the Berkeley parser (Petrov and Klein, 2007), a state-of-the-art chart parser.

This particular problem is caused by an annotation error in the original Penn Treebank that was carried through in the conversion to CCGbank.

For gold-standard parses, we remove functional tag and trace information from the Penn Treebank parses before we extract features over them, so as to simulate the conditions of an automatic parse.

The Penn Treebank features are as follows:

We compare the CCG parser of Clark and Curran (2007) with a state-of-the-art Penn Treebank (PTB) parser.

The first approach, which began in the mid-90$ and now has an extensive literature, is based on the Penn Treebank (PTB) parsing task: inferring skeletal phrase-structure trees for unseen sentences of the W8], and evaluating accuracy according to the Parseval metrics.

The formalism-based parser we use is the CCG parser of Clark and Curran (2007), which is based on CCGbank (Hockenmaier and Steedman, 2007), a CCG version of the Penn Treebank .

We also trained the parser on the broadcast-news treebank available in the OntoNotes corpus (LDC2008T04), and added sections 02-21 of the WSJ Penn treebank .

Our other test set is the standard Section 23 of the Penn treebank .

For Parsing, sentences are cased and tokenization abides to the PTB segmentation as used in the Penn treebank version 3.

For example, Higgins and Sadock (2003) find fewer than 1000 sentences with two or more explicit quantifiers in the Wall Street journal section of Penn Treebank .

Plurals form 18% of the NPs in our corpus and 20% of the nouns in Penn Treebank .

Explicit universals, on the other hand, form less than 1% of the determiners in Penn Treebank .

The experiments are conducted on Penn Treebank Wall Street Journal corpus.

Because we are trying to improve (Yatbaz et al., 2012), we select the experiment on Penn Treebank Wall Street Journal corpus in that work as our baseline and replicate it.

For instance,the gold tag perplexity of word “offers” in the Penn Treebank Wall Street Journal corpus we worked on equals to 1.966.

CCGbank was created by semiautomatically converting the Penn Treebank to CCG derivations (Hockenmaier and Steedman, 2007).

Most work has focused on POS-tagging for English using the Penn Treebank (Marcus et al., 1993), such as (Banko and Moore, 2004; Goldwater and Griffiths, 2007; Toutanova and J ohn-son, 2008; Goldberg et al., 2008; Ravi and Knight, 2009).

This generally involves working with the standard set of 45 POS-tags employed in the Penn Treebank .

Our SR-TSG parser achieves an F 1 score of 92.4% in the Wall Street Journal (WSJ) English Penn Treebank parsing task, which is a 7.7 point improvement over a conventional Bayesian TSG parser, and better than state-of-the-art discriminative reranking parsers.

We ran experiments on the Wall Street Journal (WSJ) portion of the English Penn Treebank data set (Marcus et al., 1993), using a standard data split (sections 2—21 for training, 22 for development and 23 for testing).

Our SR-TSG parser achieves an F1 score of 92.4% in the WSJ English Penn Treebank parsing task, which is a 7.7 point improvement over a conventional Bayesian TSG parser, and superior to state-of-the-art discriminative reranking parsers.

Proposition Bank (Palmer et al., 2005) adds Levin’s style predicate-argument annotation and indication of verbs’ alternations to the syntactic structures of the Penn Treebank (Marcus et al.,

Verbal predicates in the Penn Treebank (PTB) receive a label REL and their arguments are annotated with abstract semantic role labels A0-A5 or AA for those complements of the predicative verb that are considered arguments, while those complements of the verb labelled with a semantic functional label in the original PTB receive the composite semantic role label AM-X, where X stands for labels such as LOC, TMP or ADV, for locative, temporal and adverbial modifiers respectively.

SemLink1 provides mappings from PropB ank to VerbNet for the WSJ portion of the Penn Treebank .

The experiments were performed on the Penn Treebank (PTB) (Marcus et al., 1993), using a standard set of head-selection rules (Yamada

With the availability of large-scale annotated corpora such as Penn Treebank (Marcus et al., 1993), it is easy to train a high-performance dependency parser using supervised learning methods.

We conduct the experiments on the English Penn Treebank (PTB) (Marcus et al., 1993).

Current state-of-the art syntactic parsers have achieved accuracies in the range of 90% F1 on the Penn Treebank , but a range of errors remain.

Figure l: A PP attachment error in the parse output of the Berkeley parser (on Penn Treebank ).

We use the standard splits of Penn Treebank into training (sections 2-21), development (section 22) and test (section 23).

This method has been used effectively to improve parsing performance on newspaper text (McClosky et al., 2006a), as well as adapting a Penn Treebank parser to a new domain (McClosky et al., 2006b).

We have used Sections 02-21 of CCGbank (Hock-enmaier and Steedman, 2007), the CCG version of the Penn Treebank (Marcus et al., 1993), as training data for the newspaper domain.

Since the CCG lexical category set used by the supertagger is much larger than the Penn Treebank POS tag set, the accuracy of supertagging is much lower than POS tagging; hence the CCG supertagger assigns multiple supertags1 to a word, when the local context does not provide enough information to decide on the correct supertag.

Compared with the widely used Penn TreeBank annotation, the new criterion utilizes some different grammar tags and is able to effectively describe some rare language phenomena in Chinese.

The annotator still uses Penn TreeBank annotation on the English side.

In addition, HIT corpus is not applicable for MT experiment due to the problems of domain divergence, annotation discrepancy (Chinese parse tree employs a different grammar from Penn Treebank annotations) and degree of tolerance for parsing errors.