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They have made use of local and global features to deal with the instances of same token in a document.
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We propose maximizing , where is the sequence of named- entity tags assigned to the words in the sentence , and is the information that can be extracted from the whole document containing . Our system is built on a maximum entropy classifier.
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Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.
| 0 |
a classifier.
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The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
| 0 |
Figure 4 Input lattice (top) and two segmentations (bottom) of the sentence 'How do you say octopus in Japanese?'.
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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.
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The method halves the error rate in comparison to a method using the labeled examples alone.
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There are clustering approaches that assign a single POS tag to each word type.
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2 60.7 56.
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They employed a PCFG-based generative framework to make both syntactic and morphological disambiguation decisions which were not only theoretically clean and linguistically justified but also probabilistically appropriate and empirically sound.
| 0 |
To facilitate the comparison of our results to those reported by (Cohen and Smith, 2007) we use their data set in which 177 empty and “malformed”7 were removed.
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On the basis of that observation, they described a class of formalisms which they called Linear Context- Free Rewriting Systems (LCFRs), and showed they were recognizable in polynomial time and generated only semilinear languages.
| 0 |
We now turn our attention to the recognition of string languages generated by these formalisms (LCFRL's).
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Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
| 0 |
The equation for sampling a single type-level assignment Ti is given by, 0.2 0 5 10 15 20 25 30 Iteration Figure 2: Graph of the one-to-one accuracy of our full model (+FEATS) under the best hyperparameter setting by iteration (see Section 5).
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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 |
Once the lexicon has been drawn, the model proceeds similarly to the standard token-level HMM: Emission parameters θ are generated conditioned on tag assignments T . We also draw transition parameters Ï.
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they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
| 0 |
The horizontal axis in this plot represents the most significant dimension, which explains 62% of the variation.
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The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM.
| 0 |
Compared with SRILM, IRSTLM adds several features: lower memory consumption, a binary file format with memory mapping, caching to increase speed, and quantization.
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Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
| 0 |
The tokens w are generated by token-level tags t from an HMM parameterized by the lexicon structure.
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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 |
However, it is possible to personify any noun, so in children's stories or fables, i¥JJ1l.
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The overall performance of their joint framework demonstrated that a probability distribution obtained over mere syntactic contexts using a Treebank grammar and a data-driven lexicon outperformed upper bounds proposed by previous joint disambiguation systems and achieved segmentation and parsing results on a par with state-of-the-art standalone applications results.
| 0 |
The table makes clear that enriching our grammar improves the syntactic performance as well as morphological disambiguation (segmentation and POS tagging) accuracy.
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BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results.
| 0 |
Table 1 briefly describes the seven syntactic heuristics used by BABAR to resolve noun phrases.
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The three parsers were trained and tuned by their creators on various sections of the WSJ portion of the Penn Treebank.
| 0 |
The precision and recall measures (described in more detail in Section 3) used in evaluating Treebank parsing treat each constituent as a separate entity, a minimal unit of correctness.
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The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.
| 0 |
4.1 Local Features.
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It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
| 0 |
Our System Wang, Li, and Chang a. 1\!f!IP Eflltii /1\!f!J:P $1til I b. agm: I a m: c. 5 Bf is Bf 1 d. "*:t: w _t ff 1 "* :t: w_tff 1 g., , Transliteration/Translation chen2zhongl-shenl qu3 'music by Chen Zhongshen ' huang2rong2 youlyoul de dao4 'Huang Rong said soberly' zhangl qun2 Zhang Qun xian4zhang3 you2qingl shang4ren2 hou4 'after the county president You Qing had assumed the position' lin2 quan2 'Lin Quan' wang2jian4 'Wang Jian' oulyang2-ke4 'Ouyang Ke' yinl qi2 bu4 ke2neng2 rong2xu3 tai2du2 er2 'because it cannot permit Taiwan Independence so' silfa3-yuan4zhang3 lin2yang2-gang3 'president of the Judicial Yuan, Lin Yanggang' lin2zhangl-hu2 jiangl zuo4 xian4chang3 jie3shuol 'Lin Zhanghu will give an ex planation live' jin4/iang3 nian2 nei4 sa3 xia4 de jinlqian2 hui4 ting2zhi3 'in two years the distributed money will stop' gaoltangl da4chi2 ye1zi0 fen3 'chicken stock, a tablespoon of coconut flakes' you2qingl ru4zhu3 xian4fu3 lwu4 'after You Qing headed the county government' Table 5 Performance on morphological analysis.
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The authors use linguistic and annotation insights to develop a manually annotated grammar and evaluate it and finally provide a realistic evaluation in which segmentation is performed in a pipeline jointly with parsing.
| 0 |
The difference is due to more careful S-NOM NP NP NP VP VBG :: b NP restoring NP ADJP NN :: b NP NN NP NP ADJP DTJJ ADJP DTJJ NN :: b NP NP NP ADJP ADJP DTJJ J ..i NN :: b NP NP NP ADJP ADJP DTJJ NN _;� NP PRP DTJJ DTJJ J ..i _;� PRP J ..i NN _;� NP PRP DTJJ NN _;� NP PRP DTJJ J ..i role its constructive effective (b) Stanford (c) Berkeley (d) Bik el (a) Reference Figure 4: The constituent Restoring of its constructive and effective role parsed by the three different models (gold segmentation).
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The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data.
| 0 |
While automatic measures are an invaluable tool for the day-to-day development of machine translation systems, they are only a imperfect substitute for human assessment of translation quality, or as the acronym BLEU puts it, a bilingual evaluation understudy.
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In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures.
| 0 |
We also see that the increase in the size of the label sets for Head and Head+Path is far below the theoretical upper bounds given in Table 1.
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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 |
5.1 Data Sets.
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They plan on extending instance-weighting to other standard SMT components and capture the degree of generality of phrase pairs.
| 0 |
It is also worth pointing out a connection with Daum´e’s (2007) work that splits each feature into domain-specific and general copies.
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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 |
One of the strengths of the DempsterShafer model is its natural ability to recognize when several credible hypotheses are still in play.
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They proposed a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.
| 0 |
The form mnh itself can be read as at least three different verbs (“counted”, “appointed”, “was appointed”), a noun (“a portion”), and a possessed noun (“her kind”).
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It is probably the first analysis of Arabic parsing of this kind.
| 0 |
For all other recursive NPs, we add a common annotation to the POS tag of the head (recursiveNPHead).
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The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
| 0 |
Since our objective is to compare distributions of bracketing discrepancies, we do not use heuristics to prune the set of nuclei.
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The authors use linguistic and annotation insights to develop a manually annotated grammar and evaluate it and finally provide a realistic evaluation in which segmentation is performed in a pipeline jointly with parsing.
| 0 |
We are also grateful to Markus Dickinson, Ali Farghaly, Nizar Habash, Seth Kulick, David McCloskey, Claude Reichard, Ryan Roth, and Reut Tsarfaty for constructive discussions.
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In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
| 0 |
This suggests that the backoff model is as reasonable a model as we can use in the absence of further information about the expected cost of a plural form.
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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 |
(For some recent corpus-based work on Chinese abbreviations, see Huang, Ahrens, and Chen [1993].)
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The overall parsing accuracy obtained with the pseudo-projective approach is still lower than for the best projective parsers.
| 0 |
It is likely that the more complex cases, where path information could make a difference, are beyond the reach of the parser in most cases.
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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 |
Automatic scores are computed on a larger tested than manual scores (3064 sentences vs. 300–400 sentences). collected manual judgements, we do not necessarily have the same sentence judged for both systems (judges evaluate 5 systems out of the 8–10 participating systems).
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The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.
| 0 |
First, from a large corpus, we extract all the NE instance pairs.
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Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information.
| 0 |
The choice of the genre commentary resulted from the fact that an investigation of rhetorical structure, its interaction with other aspects of discourse structure, and the prospects for its automatic derivation are the key motivations for building up the corpus.
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This paper talks about Pseudo-Projective Dependency Parsing.
| 0 |
Evaluation metrics used are Attachment Score (AS), i.e. the proportion of tokens that are attached to the correct head, and Exact Match (EM), i.e. the proportion of sentences for which the dependency graph exactly matches the gold standard.
|
These clusters are computed using an SVD variant without relying on transitional structure.
| 0 |
36.
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However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.
| 0 |
BABAR uses information extraction patterns to identify contextual roles and creates four contextual role knowledge sources using unsupervised learning.
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In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold.
| 0 |
âAgreeâ is a subject control verb, which dominates another verb whose subject is the same as that of âagreeâ; the latter verb is generally the one of interest for extraction.
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The authors use linguistic and annotation insights to develop a manually annotated grammar and evaluate it and finally provide a realistic evaluation in which segmentation is performed in a pipeline jointly with parsing.
| 0 |
92 76.
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This paper presents a maximum entropy-based named entity recognizer (NER).
| 0 |
By using the output of a hand-coded system such as Proteus, MENE can improve its performance, and can even outperform IdentiFinder (Borthwick, 1999).
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The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.
| 0 |
One possibility is to use n-grams based on mutual information.
|
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 |
The results in Table 2 were achieved on the development set.
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They proposed a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.
| 0 |
Every token is independent of the others, and the sentence lattice is in fact a concatenation of smaller lattices, one for each token.
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This assumption, however, is not inherent to type-based tagging models.
| 0 |
These clusters are computed using an SVD variant without relying on transitional structure.
|
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP).
| 1 |
In this paper, we describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP).
|
They have made use of local and global features to deal with the instances of same token in a document.
| 0 |
It is unique, agrees with the maximum-likelihood distribution, and has the exponential form (Della Pietra et al., 1997): where refers to the outcome, the history (or context), and is a normalization function.
|
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information.
| 0 |
This is manifest in the lexical choices but 1 www.coli.unisb.de/â¼thorsten/tnt/ Dagmar Ziegler is up to her neck in debt.
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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 |
Evaluation of the Segmentation as a Whole.
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All the texts were annotated by two people.
| 0 |
A corpus of German newspaper commentaries has been assembled at Potsdam University, and annotated with different linguistic information, to different degrees.
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Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.
| 0 |
Chunking is not enough to find such relationships.
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This assumption, however, is not inherent to type-based tagging models.
| 0 |
Simply assigning to each word its most frequent associated tag in a corpus achieves 94.6% accuracy on the WSJ portion of the Penn Treebank.
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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 |
Recent research has demonstrated that incorporating this sparsity constraint improves tagging accuracy.
|
NER is useful in many NLP applications such as information extraction, question answering, etc .Chieu and Ng have shown that the maximum entropy framework is able to use global information directly from various sources.
| 0 |
, Sun day, then the feature DayOfTheWeek is set to 1.
|
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 |
The first m pairs have labels yi, whereas for i = m + 1, , n the pairs are unlabeled.
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It is probably the first analysis of Arabic parsing of this kind.
| 0 |
97 81.
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The AdaBoost algorithm was developed for supervised learning.
| 0 |
For example, the independence assumptions mean that the model fails to capture the dependence between specific and more general features (for example the fact that the feature full.-string=New_York is always seen with the features contains (New) and The baseline method tags all entities as the most frequent class type (organization). contains (York) and is never seen with a feature such as contains (Group) ).
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A beam search concept is applied as in speech recognition.
| 0 |
Here, we process only full-form words within the translation procedure.
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The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
| 0 |
Wu and Fung introduce an evaluation method they call nk-blind.
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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 |
Figure 1 shows sample sentences from these domains, which are widely divergent.
|
The fact that projective dependency parsers can never exactly reproduce the analyses found in non-projective tree banks is often neglected because of the relative scarcity of problematic constructions.
| 0 |
Prague Dependency Treebank (Hajiˇc et al., 2001b), Danish Dependency Treebank (Kromann, 2003), and the METU Treebank of Turkish (Oflazer et al., 2003), which generally allow annotations with nonprojective dependency structures.
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The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
| 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.
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It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
| 0 |
JA DE G O L D G R AS S SI C K NE SS DE AT H R A T 14.
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The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems.
| 0 |
In this case, this knowledge source reports that the candidate is not a viable antecedent for the anaphor.
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The PROBING data structure uses linear probing hash tables and is designed for speed.
| 0 |
This makes memory usage comparable to our PROBING model.
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Human judges also pointed out difficulties with the evaluation of long sentences.
| 0 |
For the automatic scoring method BLEU, we can distinguish three quarters of the systems.
|
Human judges also pointed out difficulties with the evaluation of long sentences.
| 0 |
We check, how likely only up to k = 20 better scores out of n = 100 would have been generated by two equal systems, using the binomial distribution: If p(0..k; n, p) < 0.05, or p(0..k; n, p) > 0.95 then we have a statistically significant difference between the systems.
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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 |
In this paper, we make a simplifying assumption of one-tag-per-word.
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The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.
| 0 |
Find keywords for each NE pair The keywords are found for each NE category pair.
|
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 |
Since foreign names can be of any length, and since their original pronunciation is effectively unlimited, the identi fication of such names is tricky.
|
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 |
We evaluated BABAR on two domains: terrorism and natural disasters.
|
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 |
2 61.7 64.
|
Finally, several coreference systems have successfully incorporated anaphoricity determination modules.
| 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).
|
Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be.
| 0 |
Somewhat surprisingly, there do not appear to be large systematic differences between linear and MAP combinations.
|
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 |
Since our goal is to perform well under these measures we will similarly treat constituents as the minimal substructures for combination.
|
Using a treebank grammar, the data-driven lexicon, and a linguistically motivated unknown-tokens handling technique their model outperformed previous pipelined, integrated or factorized systems for Hebrew morphological and syntactic processing, yielding an error reduction of 12% over the best published results so far.
| 0 |
A compatible view is presented by Charniak et al. (1996) who consider the kind of probabilities a generative parser should get from a PoS tagger, and concludes that these should be P(w|t) “and nothing fancier”.3 In our setting, therefore, the Lattice is not used to induce a probability distribution on a linear context, but rather, it is used as a common-denominator of state-indexation of all segmentations possibilities of a surface form.
|
There are clustering approaches that assign a single POS tag to each word type.
| 0 |
La ng ua ge # To ke ns # W or d Ty pe s # Ta gs E ng lis h D a ni s h D u tc h G e r m a n P or tu g u e s e S p a ni s h S w e di s h 1 1 7 3 7 6 6 9 4 3 8 6 2 0 3 5 6 8 6 9 9 6 0 5 2 0 6 6 7 8 8 9 3 3 4 1 9 1 4 6 7 4 9 2 0 6 1 8 3 5 6 2 8 3 9 3 7 2 3 2 5 2 8 9 3 1 1 6 4 5 8 2 0 0 5 7 4 5 2 5 1 2 5 4 2 2 4 7 4 1 Table 2: Statistics for various corpora utilized in experiments.
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Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.
| 0 |
In the case of adverbial reduplication illustrated in (3b) an adjective of the form AB is reduplicated as AABB.
|
This paper presents a maximum entropy-based named entity recognizer (NER).
| 0 |
4.1 Local Features.
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This paper presents methods to query N-gram language models, minimizing time and space costs.
| 0 |
IRSTLM and BerkeleyLM use this state function (and a limit of N −1 words), but it is more strict than necessary, so decoders using these packages will miss some recombination opportunities.
|
Human judges also pointed out difficulties with the evaluation of long sentences.
| 0 |
By taking the ratio of matching n-grams to the total number of n-grams in the system output, we obtain the precision pn for each n-gram order n. These values for n-gram precision are combined into a BLEU score: The formula for the BLEU metric also includes a brevity penalty for too short output, which is based on the total number of words in the system output c and in the reference r. BLEU is sensitive to tokenization.
|
They proposed a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.
| 0 |
We smooth Prf(p —* (s, p)) for rare and OOV segments (s E l, l E L, s unseen) using a “per-tag” probability distribution over rare segments which we estimate using relative frequency estimates for once-occurring segments.
|
This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers.
| 0 |
Both authors are members of the Center for Language and Speech Processing at Johns Hopkins University.
|
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
| 0 |
Often, two systems can not be distinguished with a confidence of over 95%, so there are ranked the same.
|
The three parsers were trained and tuned by their creators on various sections of the WSJ portion of the Penn Treebank.
| 0 |
If the parse contains productions from outside our grammar the machine has no direct method for handling them (e.g. the resulting database query may be syntactically malformed).
|
The approach assumes that the word reordering is restricted to a few positions in the source sentence.
| 0 |
A typical situation is shown in Figure 1.
|
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
| 0 |
Because the Bikel parser has been parameter- ized for Arabic by the LDC, we do not change the default model settings.
|
These clusters are computed using an SVD variant without relying on transitional structure.
| 0 |
4 53.7 43.
|
On the basis of that observation, they described a class of formalisms which they called Linear Context- Free Rewriting Systems (LCFRs), and showed they were recognizable in polynomial time and generated only semilinear languages.
| 0 |
M must enter a universal state and check that each of the k constant substrings are in the appropriate place (as determined by the contents of the first 2k work tapes) on the input tape.
|
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data.
| 0 |
Hence, the different averages of manual scores for the different language pairs reflect the behaviour of the judges, not the quality of the systems on different language pairs.
|
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 |
Wu and Fung introduce an evaluation method they call nk-blind.
|
They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines.
| 0 |
This leads to a linear combination of domain-specific probabilities, with weights in [0, 1], normalized to sum to 1.
|
Human judges also pointed out difficulties with the evaluation of long sentences.
| 0 |
Due to many similarly performing systems, we are not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics.
|
They have made use of local and global features to deal with the instances of same token in a document.
| 0 |
Sequence of Initial Caps (SOIC): In the sentence Even News Broadcasting Corp., noted for its accurate reporting, made the erroneous announcement., a NER may mistake Even News Broadcasting Corp. as an organization name.
|
The features were weighted within a logistic model that gave an overall weight that was applied to the phrase pair and MAP-smoothed relative-frequency estimates which were combined linearly with relative-frequency estimates from an in-domain phrase table.
| 0 |
However, it is robust, efficient, and easy to implement.4 To perform the maximization in (7), we used the popular L-BFGS algorithm (Liu and Nocedal, 1989), which requires gradient information.
|
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 |
While the first three models get three to four tags wrong, our best model gets only one word wrong and is the most accurate among the four models for this example.
|
The contextual rules are restricted and may not be applicable to every example, but the spelling rules are generally applicable and should have good coverage.
| 0 |
The question of what soft function to pick, and how to design' algorithms which optimize it, is an open question, but appears to be a promising way of looking at the problem.
|
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
| 0 |
The method just described segments dictionary words, but as noted in Section 1, there are several classes of words that should be handled that are not found in a standard dictionary.
|
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 |
This property reflects an important aspect of the underlying linguistic theory associated with the formalism.
|
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 WFST is then summed with the WFST implementing the dictionary and morphological rules, and the transitive closure of the resulting transducer is computed; see Pereira, Riley, and Sproat (1994) for an explanation of the notion of summing WFSTs.12 Conceptual Improvements over Chang et al.'s Model.
|
There are clustering approaches that assign a single POS tag to each word type.
| 0 |
This design leads to a significant reduction in the computational complexity of training and inference.
|
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