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Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs. | 0 | Second, BABAR performs reliable case resolution to identify anaphora that can be easily resolved using the lexical and syntactic heuristics described in Section 2.1. |
Foster et all describe a new approach to SMT adaptation that weights out-of-domain phrase pairs according to their relevance to the target domain, determined by both how similar to it they appear to be, and whether they belong to general language or not. | 0 | Discriminative Instance Weighting for Domain Adaptation in Statistical Machine Translation |
they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement. | 0 | The simplest approach involves scoring the various analyses by costs based on word frequency, and picking the lowest cost path; variants of this approach have been described in Chang, Chen, and Chen (1991) and Chang and Chen (1993). |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | Unfortunately, there is no standard corpus of Chinese texts, tagged with either single or multiple human judgments, with which one can compare performance of various methods. |
The main result is that the combined system can recover non-projective dependencies with a precision sufficient to give a significant improvement in overall parsing accuracy, especially with respect to the exact match criterion, leading to the best reported performance for robust non-projective parsing of Czech. | 0 | As observed by Kahane et al. (1998), any (nonprojective) dependency graph can be transformed into a projective one by a lifting operation, which replaces each non-projective arc wj wk by a projective arc wi —* wk such that wi —*∗ wj holds in the original graph. |
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy. | 0 | While building a machine translation system is a serious undertaking, in future we hope to attract more newcomers to the field by keeping the barrier of entry as low as possible. |
They proposed an unsupervised method to discover paraphrases from a large untagged corpus. | 0 | Instead, we focused on phrases and set the frequency threshold to 2, and so were able to utilize a lot of phrases while minimizing noise. |
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity. | 0 | In contrast to these approaches, our method directly incorporates these constraints into the structure of the model. |
There is no global pruning. | 0 | 2. |
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information. | 0 | It is difficult to motivate these days why one ministry should be exempt from cutbacks â at the expense of the others. |
In this work, they take a more direct approach and treat a word type and its allowed POS tags as a primary element of the model. | 0 | The P (T |Ï) distribution, in English for instance, should have very low mass for the DT (determiner) tag, since determiners are a very small portion of the vocabulary. |
The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text. | 0 | It is formally straightforward to extend the grammar to include these names, though it does increase the likelihood of overgeneration and we are unaware of any working systems that incorporate this type of name. |
Through parser combination we have reduced the precision error rate by 30% and the recall error rate by 6% compared to the best previously published result. | 0 | This is equivalent to the assumption used in probability estimation for naïve Bayes classifiers, namely that the attribute values are conditionally independent when the target value is given. |
The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text. | 0 | The relevance of the distinction between, say, phonological words and, say, dictionary words is shown by an example like rpftl_A :;!:Hfllil zhong1hua2 ren2min2 gong4he2-guo2 (China people republic) 'People's Republic of China.' |
BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results. | 0 | The types of patterns produced by AutoSlog are outlined in (Riloff, 1996). |
There is no global pruning. | 0 | We show translation results for three approaches: the monotone search (MonS), where no word reordering is allowed (Tillmann, 1997), the quasimonotone search (QmS) as presented in this paper and the IBM style (IbmS) search as described in Section 3.2. |
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs. | 0 | We suspect that the higher precision in the disasters domain may be due to its substantially larger training corpus. |
the "Potsdam Commentary Corpus" or PCC consists of 170 commentaries from Ma¨rkische Allgemeine Zeitung, a German regional daily. | 0 | The rhetorical structure annotations of PCC have all been converted to URML. |
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy. | 0 | Annotators suggested that long sentences are almost impossible to judge. |
They proposed an unsupervised method to discover paraphrases from a large untagged corpus. | 0 | As you can see in the figure, the accuracy for the domain is quite high except for the âagreeâ set, which contains various expressions representing different relationships for an IE application. |
The texts were annotated with the RSTtool. | 0 | This means that the PCC cannot grow particularly quickly. |
Across eight European languages, their approach results in an average absolute improvement of 10.4% over a state-of-the-art baseline, and 16.7% over vanilla hidden Markov models induced with the Expectation Maximization algorithm. | 0 | To provide a thorough analysis, we evaluated three baselines and two oracles in addition to two variants of our graph-based approach. |
They showed that it was useful to abstract away from the details of the formalism, and examine the nature of their derivation process as reflected by properties their trees, find that several of the formalisms considered can be seen as being closely related since they have derivation tree sets with the same structure as those produced by Context-Free Grammars. | 0 | Steedman (1986) considers Categorial Grammars in which both the operations of function application and composition may be used, and in which function can specify whether they take their arguments from their right or left. |
This topic has been getting more attention, driven by the needs of various NLP applications. | 0 | For example, in the phrase âCompany-A last week purchased rival Marshalls from Company-Bâ, the purchased company is Marshalls, not Company-B. |
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse. | 0 | For each domain, we created a semantic dictionary by doing two things. |
They used graph-based label propagation for cross-lingual knowledge transfer and used the projected labels as features in an unsupervised model. | 0 | We use two different similarity functions to define the edge weights among the foreign vertices and between vertices from different languages. |
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP). | 0 | The translation scores for the hypotheses generated with different threshold values t0 are compared to the translation scores obtained with a conservatively large threshold t0 = 10:0 . For each test series, we count the number of sentences whose score is worse than the corresponding score of the test series with the conservatively large threshold t0 = 10:0, and this number is reported as the number of search errors. |
This paper conducted research in the area of automatic paraphrase discovery. | 0 | The links can solve the problem. |
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data. | 0 | The importance of dictionaries in NERs has been investigated in the literature (Mikheev et al., 1999). |
It is probably the first analysis of Arabic parsing of this kind. | 0 | pre-processing. |
They believe that global context is useful in most languages, as it is a natural tendency for authors to use abbreviations on entities already mentioned previously. | 0 | In addition, each feature function is a binary function. |
Across eight European languages, their approach results in an average absolute improvement of 10.4% over a state-of-the-art baseline, and 16.7% over vanilla hidden Markov models induced with the Expectation Maximization algorithm. | 0 | We describe a novel approach for inducing unsupervised part-of-speech taggers for languages that have no labeled training data, but have translated text in a resource-rich language. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | The simplest approach involves scoring the various analyses by costs based on word frequency, and picking the lowest cost path; variants of this approach have been described in Chang, Chen, and Chen (1991) and Chang and Chen (1993). |
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs. | 0 | Relative pronouns with only 1 NP in scope.. |
They have made use of local and global features to deal with the instances of same token in a document. | 0 | We have estimated the performance of IdentiFinder ' 99 at 200K words of training data from the graphs. |
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university. | 0 | Clearly this poses a number of research challenges, though, such as the applicability of tag sets across different languages. |
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems. | 0 | Figure 1 reveals that an event that âdamagedâ objects may also cause injuries; a disaster that âoccurredâ may be investigated to find its âcauseâ; a disaster may âwreakâ havoc as it âcrossesâ geographic regions; and vehicles that have a âdriverâ may also âcarryâ items. |
Das and Petrov, in this paper, approached inducing unsupervised part-of-speech taggers for languages that had no labeled training data, but had translated text in a resource-rich language. | 0 | This vector tx is constructed for every word in the foreign vocabulary and will be used to provide features for the unsupervised foreign language POS tagger. |
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs. | 0 | First, we describe how the caseframes are represented and learned. |
Due to many similarly performing systems, the author was not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics. | 0 | The main disadvantage of manual evaluation is that it is time-consuming and thus too expensive to do frequently. |
This paper presents a maximum entropy-based named entity recognizer (NER). | 0 | Frequency is calculated by counting the number of distinct previous tokens that each token has (e.g., if Electric Corp. is seen 3 times, and Manufacturing Corp. is seen 5 times during training, and Corp. is not seen with any other preceding tokens, then the âfrequencyâ of Corp. is 2). |
They believe that global context is useful in most languages, as it is a natural tendency for authors to use abbreviations on entities already mentioned previously. | 0 | Similarly, the tokens and are tested against each list, and if found, a corresponding feature will be set to 1. |
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform. | 0 | We carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs. |
The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text. | 0 | This style of naming is never required and seems to be losing currency. |
They used graph-based label propagation for cross-lingual knowledge transfer and used the projected labels as features in an unsupervised model. | 0 | Across eight European languages, our approach results in an average absolute improvement of 10.4% over a state-of-the-art baseline, and 16.7% over vanilla hidden Markov models induced with the Expectation Maximization algorithm. |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | The results in Table 1 show PROBING is 81% faster than TRIE, which is in turn 31% faster than the fastest baseline. |
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity. | 0 | This alters generation of T as follows: n P (T |Ï) = n P (Ti|Ï) i=1 Note that this distribution captures the frequency of a tag across word types, as opposed to tokens. |
This paper talks about Unsupervised Models for Named Entity Classification. | 0 | In the namedentity problem each example is a (spelling,context) pair. |
Here we present two algorithms. | 0 | The algorithm in Fig. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | 7 Big 5 is the most popular Chinese character coding standard in use in Taiwan and Hong Kong. |
In this paper, the authors are of the opinion that the sequence models-based approaches usually treat token-level tag assignment as the primary latent variable. | 0 | For other languages, we use the CoNLL-X multilingual dependency parsing shared task corpora (Buchholz and Marsi, 2006) which include gold POS tags (used for evaluation). |
Explanations for this phenomenon are relative informativeness of lexicalization, insensitivity to morphology and the effect of variable word order and these factors lead to syntactic disambiguation. | 0 | markBaseNP indicates these non-recursive nominal phrases. |
A beam search concept is applied as in speech recognition. | 0 | A procedural definition to restrict1In the approach described in (Berger et al., 1996), a mor phological analysis is carried out and word morphemes rather than full-form words are used during the search. |
Due to many similarly performing systems, the author was not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics. | 0 | We assumed that such a contrastive assessment would be beneficial for an evaluation that essentially pits different systems against each other. |
For the perplexity and translation tasks, we used SRILM to build a 5-gram English language model on 834 million tokens from Europarl v6 (Koehn, 2005) and the 2011 Workshop on Machine Translation News Crawl corpus with duplicate lines removed. | 0 | Juri Ganitkevitch answered questions about Joshua. |
The departure from the traditional token-based tagging approach allow them to explicitly capture type-level distributional properties of valid POS tag assignments as part of the model. | 0 | Across all languages, +PRIOR consistently outperforms 1TW, reducing error on average by 9.1% and 5.9% on best and median settings respectively. |
The AdaBoost algorithm was developed for supervised learning. | 0 | For t = 1, T and for j = 1, 2: where 4 = exp(-jg'(xj,i)). practice, this greedy approach almost always results in an overall decrease in the value of Zco. |
Explanations for this phenomenon are relative informativeness of lexicalization, insensitivity to morphology and the effect of variable word order and these factors lead to syntactic disambiguation. | 0 | 52 77. |
They have made use of local and global features to deal with the instances of same token in a document. | 0 | Case and Zone of and : Similarly, if (or ) is initCaps, a feature (initCaps, zone) (or (initCaps, zone) ) is set to 1, etc. Token Information: This group consists of 10 features based on the string , as listed in Table 1. |
This architecture provides a uniform framework in which it is easy to incorporate not only listed dictionary entries but also morphological derivatives, and models for personal names and foreign names in transliteration. | 0 | com t 700 Mountain Avenue, 2d451, Murray Hill, NJ 07974, USA. |
They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines. | 0 | Je voudrais pr´eciser, a` l’adresse du commissaire Liikanen, qu’il n’est pas ais´e de recourir aux tribunaux nationaux. |
This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design. | 0 | Despite their simplicity, uni- gram weights have been shown as an effective feature in segmentation models (Dyer, 2009).13 The joint parser/segmenter is compared to a pipeline that uses MADA (v3.0), a state-of-the-art Arabic segmenter, configured to replicate ATB segmentation (Habash and Rambow, 2005). |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | This representation gives ir, an appropriate morphological decomposition, pre serving information that would be lost by simply listing ir, as an unanalyzed form. |
In this paper, Ben and Riloff present a coreference resolver called BABAR that focuses on the use of contextual-role knowledge for coreference resolution. | 0 | The Recency KS computes the distance between the candidate and the anaphor relative to its scope. |
Through parser combination we have reduced the precision error rate by 30% and the recall error rate by 6% compared to the best previously published result. | 0 | Because we are working with only three parsers, the only situation in which context will help us is when it can indicate we should choose to believe a single parser that disagrees with the majority hypothesis instead of the majority hypothesis itself. |
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse. | 0 | The rationale for treating these semantic labels differently is that they are specific and reliable (as opposed to the WordNet classes, which are more coarse and more noisy due to polysemy). |
Explanations for this phenomenon are relative informativeness of lexicalization, insensitivity to morphology and the effect of variable word order and these factors lead to syntactic disambiguation. | 0 | âNP NP PP R) and âNP NP ADJP R) are both iDafa attachment. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | where the husband's family name is optionally prepended to the woman's full name; thus ;f:*lf#i xu3lin2-yan2hai3 would represent the name that Ms. Lin Yanhai would take if she married someone named Xu. |
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity. | 0 | In this paper, we make a simplifying assumption of one-tag-per-word. |
This paper conducted research in the area of automatic paraphrase discovery. | 0 | We are focusing on phrases which have two Named Entities (NEs), as those types of phrases are very important for IE applications. |
The three parsers were trained and tuned by their creators on various sections of the WSJ portion of the Penn Treebank. | 0 | Parser 3, the most accurate parser, was chosen 71% of the time, and Parser 1, the least accurate parser was chosen 16% of the time. |
Here we present two algorithms. | 0 | In Input: {(x1,i, Initialize: Vi, j : e(xi) = 0. |
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs. | 0 | The anaphor and antecedent appear in boldface. |
In this paper, Ben and Riloff present a coreference resolver called BABAR that focuses on the use of contextual-role knowledge for coreference resolution. | 0 | Relative pronouns with only 1 NP in scope.. |
In this paper, Ben and Riloff present a coreference resolver called BABAR that focuses on the use of contextual-role knowledge for coreference resolution. | 0 | For example, co-occurring caseframes may reflect synonymy (e.g., â<patient> kidnappedâ and â<patient> abductedâ) or related events (e.g., â<patient> kidnappedâ and â<patient> releasedâ). |
There are clustering approaches that assign a single POS tag to each word type. | 0 | Specifically, for the ith word type, the set of token-level tags associated with token occurrences of this word, denoted t(i), must all take the value Ti to have nonzero mass. Thus in the context of Gibbs sampling, if we want to block sample Ti with t(i), we only need sample values for Ti and consider this setting of t(i). |
A large number of rules are needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. | 0 | Each xii is a member of X, where X is a set of possible features. |
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers. | 0 | Without using the same test corpus, direct comparison is obviously difficult; fortunately, Chang et al. include a list of about 60 sentence fragments that exemplify various categories of performance for their system. |
We chose one of four labels for each example: location, person, organization, or noise where the noise category was used for items that were outside the three categories. | 0 | In addition to the named-entity string (Maury Cooper or Georgia), a contextual predictor was also extracted. |
Foster et all describe a new approach to SMT adaptation that weights out-of-domain phrase pairs according to their relevance to the target domain, determined by both how similar to it they appear to be, and whether they belong to general language or not. | 0 | Standard SMT systems have a hierarchical parameter structure: top-level log-linear weights are used to combine a small set of complex features, interpreted as log probabilities, many of which have their own internal parameters and objectives. |
We chose one of four labels for each example: location, person, organization, or noise where the noise category was used for items that were outside the three categories. | 0 | Unlabeled examples in the named-entity classification problem can reduce the need for supervision to a handful of seed rules. |
The departure from the traditional token-based tagging approach allow them to explicitly capture type-level distributional properties of valid POS tag assignments as part of the model. | 0 | They are set to fixed constants. |
It is probably the first analysis of Arabic parsing of this kind. | 0 | 72 77. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | The segmenter handles the grouping of hanzi into words and outputs word pronunciations, with default pronunciations for hanzi it cannot group; we focus here primarily on the system's ability to segment text appropriately (rather than on its pronunciation abilities). |
they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement. | 0 | orthographic words are thus only a starting point for further analysis and can only be regarded as a useful hint at the desired division of the sentence into words. |
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data. | 0 | Given the limited number of judgements we received, we did not try to evaluate this. |
Their results suggested that it was possible to learn accurate POS taggers for languages which did not have any annotated data, but have translations into a resource-rich language. | 0 | Of course, we are primarily interested in applying our techniques to languages for which no labeled resources are available. |
They showed the efficacy of graph-based label propagation for projecting part-of-speech information across languages. | 0 | Graph construction for structured prediction problems such as POS tagging is non-trivial: on the one hand, using individual words as the vertices throws away the context necessary for disambiguation; on the other hand, it is unclear how to define (sequence) similarity if the vertices correspond to entire sentences. |
Combining multiple highly-accurate independent parsers yields promising results. | 0 | Similarly Figures 1 and 2 show how the isolated constituent precision varies by sentence length and the size of the span of the hypothesized constituent. |
For the perplexity and translation tasks, we used SRILM to build a 5-gram English language model on 834 million tokens from Europarl v6 (Koehn, 2005) and the 2011 Workshop on Machine Translation News Crawl corpus with duplicate lines removed. | 0 | We offer a state function s(wn1) = wn� where substring wn� is guaranteed to extend (to the right) in the same way that wn1 does for purposes of language modeling. |
The approach has been successfully tested on the 8 000-word Verbmobil task. | 0 | The search starts in hypothesis (f;g; 0) and ends in the hypotheses (f1; ; Jg; j), with j 2 f1; ; Jg. |
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy. | 0 | 0.2 0.1 0.0 -0.1 25 26 27 28 29 30 31 32 -0.2 -0.3 •systran • ntt 0.5 0.4 0.3 0.2 0.1 0.0 -0.1 -0.2 -0.3 20 21 22 23 24 25 26 Fluency Fluency •systran •nrc rali 25 26 27 28 29 30 31 32 0.2 0.1 0.0 -0.1 -0.2 -0.3 cme p � 20 21 22 23 24 25 26 0.5 0.4 0.3 0.2 0.1 0.0 -0.1 -0.2 -0.3 Figure 14: Correlation between manual and automatic scores for English-French 119 In Domain Out of Domain •upv Adequacy -0.9 0.3 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 •upv 23 24 25 26 27 28 29 30 31 32 •upc-mr •utd •upc-jmc •uedin-birch •ntt •rali •uedin-birch 16 17 18 19 20 21 22 23 24 25 26 27 Adequacy •upc-mr 0.4 0.3 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 English-Spanish Fluency •ntt •nrc •rali •uedin-birch -0.2 -0.3 -0.5 •upv 16 17 18 19 20 21 22 23 24 25 26 27 -0.4 nr • rali Fluency -0.4 •upc-mr utd •upc-jmc -0.5 -0.6 •upv 23 24 25 26 27 28 29 30 31 32 0.2 0.1 -0.0 -0.1 -0.2 -0.3 0.3 0.2 0.1 -0.0 -0.1 -0.6 -0.7 Figure 15: Correlation between manual and automatic scores for English-Spanish 120 English-German In Domain Out of Domain Adequacy Adequacy 0.3 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 •upv -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 •upv 0.5 0.4 •systran •upc-mr • •rali 0.3 •ntt 0.2 0.1 -0.0 -0.1 •systran •upc-mr -0.9 9 10 11 12 13 14 15 16 17 18 19 6 7 8 9 10 11 Fluency 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 •upv -0.5 •upv •systran •upc-mr • Fluency 0.4 0.3 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 •systran •ntt |
The AdaBoost algorithm was developed for supervised learning. | 0 | It's not clear how to apply these methods in the unsupervised case, as they required cross-validation techniques: for this reason we use the simpler smoothing method shown here. input to the unsupervised algorithm is an initial, "seed" set of rules. |
Their empirical results demonstrate that the type-based tagger rivals state-of-the-art tag-level taggers which employ more sophisticated learning mechanisms to exploit similar constraints. | 0 | 5.1 Data Sets. |
In order to handle the necessary word reordering as an optimization problem within the dynamic programming approach, they describe a solution to the traveling salesman problem (TSP) which is based on dynamic programming. | 0 | A procedural definition to restrict1In the approach described in (Berger et al., 1996), a mor phological analysis is carried out and word morphemes rather than full-form words are used during the search. |
Explanations for this phenomenon are relative informativeness of lexicalization, insensitivity to morphology and the effect of variable word order and these factors lead to syntactic disambiguation. | 0 | The resulting structural differences between tree- banks can account for relative differences in parsing performance. |
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98). | 0 | (5) and ht into Equ. |
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university. | 0 | The PCC is not the result of a funded project. |
They focused on phrases which two Named Entities, and proceed in two stages. | 0 | This problem arises because our keywords consist of only one word. |
This corpus has several advantages: it is annotated at different levels. | 0 | Having explained the various layers of annotation in PCC, we now turn to the question what all this might be good for. |
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