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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.
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For each token, three types of features may be taken into account: the word form; the part-of-speech assigned by an automatic tagger; and labels on previously assigned dependency arcs involving the token – the arc from its head and the arcs to its leftmost and rightmost dependent, respectively.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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The breakdown of the different types of words found by ST in the test corpus is given in Table 3.
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98).
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Taking /V, to be the number of examples an algorithm classified correctly (where all gold standard items labeled noise were counted as being incorrect), we calculated two measures of accuracy: See Tab.
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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Here, we conThis model is equivalent to the standard HMM ex cept that it enforces the one-word-per-tag constraint.
This topic has been getting more attention, driven by the needs of various NLP applications.
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This limits the number of NE category pairs to 2,000 and the number of NE pair instances to 0.63 million.
Here both parametric and non-parametric models are explored.
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In the interest of testing the robustness of these combining techniques, we added a fourth, simple nonlexicalized PCFG parser.
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.
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This enables us to build a high performance NER without using separate classifiers to take care of global consistency or complex formulation on smoothing and backoff models (Bikel et al., 1997).
The PROBING data structure uses linear probing hash tables and is designed for speed.
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It is generally considered to be fast (Pauls 29 − 1 probabilities and 2' − 2 non-zero backoffs. and Klein, 2011), with a default implementation based on hash tables within each trie node.
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.
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These 140 NE categories are designed by extending MUC’s 7 NE categories with finer sub-categories (such as Company, Institute, and Political Party for Organization; and Country, Province, and City for Location) and adding some new types of NE categories (Position Title, Product, Event, and Natural Object).
This paper talks about Pseudo-Projective Dependency Parsing.
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Pseudo-Projective Dependency Parsing
There is no global pruning.
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We can do that . IbmS: Yes, wonderful.
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data.
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This data set of manual judgements should provide a fruitful resource for research on better automatic scoring methods.
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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Turning now to (1), we have the similar problem that splitting.into.ma3 'horse' andlu4 'way' is more costly than retaining this as one word .ma3lu4 'road.'
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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In the denomi 11 We have two such lists, one containing about 17,000 full names, and another containing frequencies of.
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.
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Supervised methods have been applied quite successfully to the full MUC named-entity task (Bikel et al. 97).
The three parsers were trained and tuned by their creators on various sections of the WSJ portion of the Penn Treebank.
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The difference in precision between similarity and Bayes switching techniques is significant, but the difference in recall is not.
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.
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This is because our corpus is not annotated, and hence does not distinguish between the various words represented by homographs, such as, which could be /adv jiangl 'be about to' orInc jiang4 '(military) general'-as in 1j\xiao3jiang4 'little general.'
The AdaBoost algorithm was developed for supervised learning.
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For example, take ..., says Maury Cooper, a vice president at S.&P.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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Figure 5 shows how this model is implemented as part of the dictionary WFST.
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.
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For the TM, this is: where cI(s, t) is the count in the IN phrase table of pair (s, t), po(s|t) is its probability under the OUT TM, and cI(t) = "s, cI(s', t).
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.
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The local feature groups are: Non-Contextual Feature: This feature is set to 1 for all tokens.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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Applications such as machine translation use language model probability as a feature to assist in choosing between hypotheses.
There is no global pruning.
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The computing time, the number of search errors, and the multi-reference WER (mWER) are shown as a function of t0.
They focused on phrases which two Named Entities, and proceed in two stages.
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More examples are shown in Figure 5.
They plan on extending instance-weighting to other standard SMT components and capture the degree of generality of phrase pairs.
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We carried out translation experiments in two different settings.
They have made use of local and global features to deal with the instances of same token in a document.
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Recently, statistical NERs have achieved results that are comparable to hand-coded systems.
Finally, several coreference systems have successfully incorporated anaphoricity determination modules.
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Most computational models for coreference resolution rely on properties of the anaphor and candidate antecedent, such as lexical matching, grammatical and syntactic features, semantic agreement, and positional information.
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.
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For IE, the system must be able to distinguish between semantically similar noun phrases that play different roles in an event.
The corpus was annoted with different linguitic information.
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Reiche’s colleagues will make sure that the concept is waterproof.
This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design.
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To combat the proliferation of parsing edges, we prune the lattices according to a hand-constructed lexicon of 31 clitics listed in the ATB annotation guidelines (Maamouri et al., 2009a).
Their results show that their high performance NER use less training data than other systems.
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He used an additional maximum entropy classifier that tries to correct mistakes by using reference resolution.
They used graph-based label propagation for cross-lingual knowledge transfer and used the projected labels as features in an unsupervised model.
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We evaluate our approach on eight European languages (§6), and show that both our contributions provide consistent and statistically significant improvements.
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.
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However, some caveats are in order in comparing this method (or any method) with other approaches to seg­ mentation reported in the literature.
The second algorithm builds on a boosting algorithm called AdaBoost.
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AdaBoost.MH can be applied to the problem using these pseudolabels in place of supervised examples.
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.
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Lilo CFG's, TAG's, and HG's the derivation tree set of a MCTAG will be a local set.
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.
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of Tokens No.
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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Following the setup of Johnson (2007), we use the whole of the Penn Treebank corpus for training and evaluation on English.
Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be.
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Daum´e (2007) applies a related idea in a simpler way, by splitting features into general and domain-specific versions.
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.
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An edge indicates that the two features must have the same label.
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.
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If two systems’ scores are close, this may simply be a random effect in the test data.
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data.
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The evaluation framework for the shared task is similar to the one used in last year’s shared task.
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98).
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Set the decision list to include all rules whose (smoothed) strength is above some threshold Pmin.
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.
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The last affix in the list is the nominal plural f, men0.20 In the table are the (typical) classes of words to which the affix attaches, the number found in the test corpus by the method, the number correct (with a precision measure), and the number missed (with a recall measure).
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold.
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One possibility is to use n-grams based on mutual information.
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.
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Such analyzers propose multiple segmentation possibilities and their corresponding analyses for a token in isolation but have no means to determine the most likely ones.
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.
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However, there is again local grammatical information that should favor the split in the case of (1a): both .ma3 'horse' and .ma3 lu4 are nouns, but only .ma3 is consistent with the classifier pil, the classifier for horses.21 By a similar argument, the preference for not splitting , lm could be strengthened in (lb) by the observation that the classifier 1'1* tiao2 is consistent with long or winding objects like , lm ma3lu4 'road' but not with,ma3 'horse.'
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.
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12 For English, our Evalb implementation is identical to the most recent reference (EVALB20080701).
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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Storing state therefore becomes a time-space tradeoff; for example, we store state with partial hypotheses in Moses but not with each phrase.
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.
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10) and trained both EM and L-BFGS for 1000 iterations.
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.
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Borth- wick (1999) successfully made use of other hand- coded systems as input for his MENE system, and achieved excellent results.
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.
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(b) POS tagging accuracy is lowest for maSdar verbal nouns (VBG,VN) and adjectives (e.g., JJ).
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.
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Lexicon Feature: The string of the token is used as a feature.
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.
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The dependency graph in Figure 1 satisfies all the defining conditions above, but it fails to satisfy the condition ofprojectivity (Kahane et al., 1998): The arc connecting the head jedna (one) to the dependent Z (out-of) spans the token je (is), which is not dominated by jedna.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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More complex approaches such as the relaxation technique have been applied to this problem Fan and Tsai (1988}.
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.
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While processing the source sentence monotonically, the initial state I is entered whenever there are no uncovered positions to the left of the rightmost covered position.
The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.
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The links can solve the problem.
Their method did not assume any knowledge about the target language, making it applicable to a wide array of resource-poor languages.
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Because there might be some controversy about the exact definitions of such universals, this set of coarse-grained POS categories is defined operationally, by collapsing language (or treebank) specific distinctions to a set of categories that exists across all languages.
A beam search concept is applied as in speech recognition.
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The alignment mapping is j ! i = aj from source position j to target position i = aj . The use of this alignment model raises major problems if a source word has to be aligned to several target words, e.g. when translating German compound nouns.
An extended lexicon model is defined, and its likelihood is compared to a baseline lexicon model, which takes only single-word dependencies into account.
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Using these states, we define partial hypothesis extensions, which are of the following type: (S0;C n fjg; j0) !
This paper conducted research in the area of automatic paraphrase discovery.
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If the expression is longer or complicated (like “A buys B” and “A’s purchase of B”), it is called “paraphrase”, i.e. a set of phrases which express the same thing or event.
The PROBING data structure uses linear probing hash tables and is designed for speed.
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Memory usage in PROBING is high, though SRILM is even larger, so where memory is of concern we recommend using TRIE, if it fits in memory.
This paper talks about Unsupervised Models for Named Entity Classification.
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The approach builds from an initial seed set for a category, and is quite similar to the decision list approach described in (Yarowsky 95).
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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Finally, we show that in application settings, the absence of gold segmentation lowers parsing performance by 2–5% F1.
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.
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Matsoukas et al (2009) generalize it by learning weights on sentence pairs that are used when estimating relative-frequency phrase-pair probabilities.
While many systems had similar performance, the results offered interesting insights, especially, about the relative performance of statistical and rule-based systems.
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We collected around 300–400 judgements per judgement type (adequacy or fluency), per system, per language pair.
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems.
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The probabilities are incorporated into the DempsterShafer model using Equation 1.
The manual evaluation of scoring translation on a graded scale from 1–5 seems to be very hard to perform.
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The BLEU metric, as all currently proposed automatic metrics, is occasionally suspected to be biased towards statistical systems, especially the phrase-based systems currently in use.
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.
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The human judges were presented with the following definition of adequacy and fluency, but no additional instructions:
Human judges also pointed out difficulties with the evaluation of long sentences.
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This data set of manual judgements should provide a fruitful resource for research on better automatic scoring methods.
They proposed a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.
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The addition of vertical markovization enables non-pruned models to outperform all previously reported re12Cohen and Smith (2007) make use of a parameter (α) which is tuned separately for each of the tasks.
Here we present two algorithms.
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(Blum and Mitchell 98) go on to give PAC results for learning in the cotraining case.
While many systems had similar performance, the results offered interesting insights, especially, about the relative performance of statistical and rule-based systems.
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Given a set of n sentences, we can compute the sample mean x� and sample variance s2 of the individual sentence judgements xi: The extend of the confidence interval [x−d, x+df can be computed by d = 1.96 ·�n (6) Pairwise Comparison: As for the automatic evaluation metric, we want to be able to rank different systems against each other, for which we need assessments of statistical significance on the differences between a pair of systems.
These clusters are computed using an SVD variant without relying on transitional structure.
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(2010) and the posterior regular- ization HMM of Grac¸a et al.
This paper presents a maximum entropy-based named entity recognizer (NER).
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Sentence (2) and (3) help to disambiguate one way or the other.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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It is. based on the traditional character set rather than the simplified character set used in Singapore and Mainland China.
It is probably the first analysis of Arabic parsing of this kind.
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07 95.
This paper talks about Unsupervised Models for Named Entity Classification.
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To prevent this we "smooth" the confidence by adding a small value, e, to both W+ and W_, giving at = Plugging the value of at from Equ.
This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design.
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85 82.
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.
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Furthermore, the combination of pruning and vertical markovization of the grammar outperforms the Oracle results reported by Cohen and Smith.
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.
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Nonetheless, the prospect of a network of annotated discourse resources seems particularly promising if not only a single annotation layer is used but a whole variety of them, so that a systematic search for correlations between them becomes possible, which in turn can lead to more explanatory models of discourse structure.
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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wo rd => na m e 2.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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The first concerns how to deal with ambiguities in segmentation.
The approach assumes that the word reordering is restricted to a few positions in the source sentence.
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2.1 Inverted Alignments.
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.
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However, it is unlikely that other occurrences of News Broadcasting Corp. in the same document also co-occur with Even.
They showed the efficacy of graph-based label propagation for projecting part-of-speech information across languages.
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The POS distributions over the foreign trigram types are used as features to learn a better unsupervised POS tagger (§5).
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.
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We are especially grateful to Taylor Berg- Kirkpatrick for running additional experiments.
Their method did not assume any knowledge about the target language, making it applicable to a wide array of resource-poor languages.
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Supervised part-of-speech (POS) taggers, for example, approach the level of inter-annotator agreement (Shen et al., 2007, 97.3% accuracy for English).
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.
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The Leaf Ancestor metric measures the cost of transforming guess trees to the reference (Sampson and Babarczy, 2003).
The texts were annotated with the RSTtool.
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A different notion of information structure, is used in work such as that of (?), who tried to characterize felicitous constituent ordering (theme choice, in particular) that leads to texts presenting information in a natural, “flowing” way rather than with abrupt shifts of attention.
They extended previous work on discriminative weighting by using a finer granularity, focusing on the properties of instances rather than corpus components, and used simpler training procedure.
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The reference medicine for Silapo is EPREX/ERYPO, which contains epoetin alfa.
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.
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The original OUT counts co(s, t) are weighted by a logistic function wλ(s, t): To motivate weighting joint OUT counts as in (6), we begin with the “ideal” objective for setting multinomial phrase probabilities 0 = {p(s|t), dst}, which is the likelihood with respect to the true IN distribution pi(s, t).
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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A crucial difference is that the number of parameters is greatly reduced as is the number of variables that are sampled during each iteration.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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i..f,..
They plan on extending instance-weighting to other standard SMT components and capture the degree of generality of phrase pairs.
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There has also been some work on adapting the word alignment model prior to phrase extraction (Civera and Juan, 2007; Wu et al., 2005), and on dynamically choosing a dev set (Xu et al., 2007).
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.
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In this case nonalpha is the string formed by removing all upper/lower case letters from the spelling (e.g., for Thomas E. Petry nonalpha= .
This paper conducted research in the area of automatic paraphrase discovery.
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As the two NE categories are the same, we can’t differentiate phrases with different orders of par ticipants – whether the buying company or the to-be-bought company comes first.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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Saving state allows our code to walk the data structure exactly once per query.
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.
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As we shall see, most of the linked sets are paraphrases.
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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Each learner is free to pick the labels for these instances.
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.
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In other words, the set of hidden states F was chosen to be the fine set of treebank tags.