6Even if some of the sentences we are learning from are marked in terms of word order , the ratios allow us to still learn the predominant order, since the marked order should occur much less frequently and the ratio will remain low.

Pulman (1997) also uses information about parallelism to predict word order .

and Klabunde (2000) describe a sentence planner for German that annotates the propositional input with discourse-related features in order to determine the focus, and thus influence word order and accentuation.

If we limit ourselves to single sentences, the task for the model is then to choose the string that is closest to the “default” expected word order (i.e.

There are many factors that influence word order , e. g. humanness, definiteness, linear order of grammatical functions, givenness, focus, constituent weight.

It is common knowledge that information status1 (henceforth, IS) has a strong influence on syntax and word order ; for instance, in inversions, where the subject follows some preposed element, Birner (1994) reports that the preposed element must not be newer in the discourse than the subject.

We believe, however, that despite this shortcoming, we can still take advantage of some of the insights gained from looking at the influence of IS on word order .

They show that MST parsing is almost as accurate as cubic-time dependency parsing in the case of English, and that it is more accurate with free word order languages.

It would also be interesting to apply these models to target languages that have free word order , which would presumably benefit more from the flexibility of non-projective dependency models.

Finally, dependency models are more flexible and account for (non-projective) head-modifier relations that CFG models fail to represent adequately, which is problematic with certain types of grammatical constructions and with free word order languages,

It is also linguistically desirable in the case of free word order languages such as Czech, Dutch, and German.

with relatively rigid word order such as English, there may be some concern that searching the space of non-projective dependency trees, which is considerably larger than the space of projective dependency trees, would yield poor performance.

(2007) sidestep the need to operate large-scale word order changes during decoding (and thus lessening the need for syntactic decoding) by rearranging input words in the training data to match the syntactic structure of the target language.

For example, Chinese-align had fewer problems with word order , and most of those were due to local word-order problems.

Word order mixed up.

Garbled word order was chosen for 21-24% of the target-language system Who/W hat errors, but only 9% of the source-language system Who/W hat errors.

The source-language word order problems tended to be local, within-phrase errors (e. g., “the dispute over frozen funds” was translated as “the freezing of disputes”).

This has the effect that the order of the nodes i and j in the appended list 2 + B is reversed compared to the original word order in the sentence.

It is important to note that SWAP is only permissible when the two nodes on top of the stack are in the original word order , which prevents the same two nodes from being swapped more than once, and when the leftmost node i is distinct from the root node 0.

LEFT-ARC; or RIGHT-ARC; to subtrees whose yields are not adjacent according to the original word order .

However, topological fields explain a higher level of structure pertaining to clause-level word order , and we hypothesize that lexicalization is unlikely to be helpful.

Topic focus ordering and word order constraints that are sensitive to phenomena other than grammatical function produce discontinuous constituents, which are not naturally modelled by projective (i.e., without crossing branches) phrase structure trees.

Hocken-maier (2006) has translated the German TIGER corpus (Brants et al., 2002) into a CCG—based treebank to model word order variations in German.

Topological fields are useful, because while Germanic word order is relatively free with respect to grammatical functions, the order of the topological fields is strict and unvarying.

Our data analysis has confirmed that each of the feature groups contributes to the overall success of the RTE metric, and that its gains come from its better success at abstracting away from valid variation (such as word order or lexical substitution), while still detecting major semantic divergences.

The first example (top) shows a good translation that is erroneously assigned a low score by METEORR because (a) it cannot align fact and reality (METEORR aligns only synonyms) and (b) it punishes the change of word order through its “penalty” term.

The human rater’s favorite translation deviates considerably from the reference in lexical choice, syntactic structure, and word order , for which it is punished by MTR (rank 3/5).

(2006) have identified a number of problems with BLEU and related n-gram-based scores: (1) BLEU-like metrics are unreliable at the level of individual sentences due to data sparsity; (2) BLEU metrics can be “gamed” by permuting word order ; (3) for some corpora and languages, the correlation to human ratings is very low even at the system level; (4) scores are biased towards statistical MT; (5) the quality gap between MT and human translations is not reflected in equally large BLEU differences.

This paper investigates the class of Tree-Tuple MCTAG with Shared Nodes, TT-MCTAG for short, an extension of Tree Adjoining Grammars that has been proposed for natural language processing, in particular for dealing with discontinuities and word order variation in languages such as German.

TT-MCTAG were introduced to deal with free word order phenomena in languages such as German.

Such a language has been proposed as an abstract description of the scrambling phenomenon as found in German and other free word order languages,

rely solely on the surface sequence of words to determine similarity between summaries, but delves into what could be called a shallow semantic structure, comprising thematic roles such as subject and object, it is likely to notice identity of meaning where such identity is obscured by variations in word order .

As a result, then, a lot of effort was put into developing metrics that can identify similar content despite non-similar form, which naturally led to the application of linguistically-oriented approaches that look beyond surface word order .

C-structure represents the word order of the surface string and the hierarchical organisation of phrases in terms of trees.