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Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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We can check, what the consequences of less manual annotation of results would have been: With half the number of manual judgements, we can distinguish about 40% of the systems, 10% less.
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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.
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(2009), who also incorporate a sparsity constraint, but does via altering the model objective using posterior regularization.
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they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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7 Big 5 is the most popular Chinese character coding standard in use in Taiwan and Hong Kong.
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The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.
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Applications The discovered paraphrases have multiple applications.
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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.
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These make left-to-right query patterns convenient, as the application need only provide a state and the word to append, then use the returned state to append another word, etc.
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This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers.
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When this metric is less than 0.5, we expect to incur more errors' than we will remove by adding those constituents to the parse.
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This paper conducted research in the area of automatic paraphrase discovery.
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This remains as future work.
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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.
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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.
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Due to many similarly performing systems, they are not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics.
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In-domain Systran scores on this metric are lower than all statistical systems, even the ones that have much worse human scores.
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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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Even when there is training data available in the domain of interest, there is often additional data from other domains that could in principle be used to improve performance.
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The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.
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One is that smaller sets sometime have meaningless keywords, like âstrengthâ or âaddâ in the CC-domain, or âcompareâ in the PC-domain.
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In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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Finally, as suming a simple bigram backoff model, we can derive the probability estimate for the particular unseen word i¥1J1l.
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Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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Systran submitted their commercial rule-based system that was not tuned to the Europarl corpus.
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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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We would like to thank Ryan McDonald for numerous discussions on this topic.
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Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.
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Along with locking and background kernel operations such as prefaulting, this explains why wall time is not one-eighth that of the single-threaded case. aLossy compression with the same weights. bLossy compression with retuned weights. the non-lossy options.
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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.
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Tsarfaty (2006) was the first to demonstrate that fully automatic Hebrew parsing is feasible using the newly available 5000 sentences treebank.
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They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines.
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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).
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Instance-weighting approach improved over a wide range of baselines, giving gains of over 2 BLEU points over the best non-adapted baseline.
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The second setting uses the news-related subcorpora for the NIST09 MT Chinese to English evaluation8 as IN, and the remaining NIST parallel Chinese/English corpora (UN, Hong Kong Laws, and Hong Kong Hansard) as OUT.
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they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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(b) F.i'JJI!
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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.
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8 66.4 52.
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Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.
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The hash variant is a reverse trie with hash tables, a more memory-efficient version of SRILM’s default.
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Human judges also pointed out difficulties with the evaluation of long sentences.
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The bias of automatic methods in favor of statistical systems seems to be less pronounced on out-of-domain test data.
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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.
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Consider the following sentences: (a) Jose Maria Martinez, Roberto Lisandy, and Dino Rossy, who were staying at a Tecun Uman hotel, were kidnapped by armed men who took them to an unknown place.
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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.
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Though we do not directly compare state implementations, performance metrics in Table 1 indicate our overall method is faster.
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The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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), and thosethat begin with a verb (� ub..i �u _..
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A beam search concept is applied as in speech recognition.
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In Section 2, we brie y review our approach to statistical machine translation.
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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.
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Second, we identified the 100 most frequent nouns in the training corpus and manually labeled them with semantic tags.
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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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For each language, we took the same number of sentences from the bitext as there are in its treebank, and trained a supervised feature-HMM.
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Finally, several coreference systems have successfully incorporated anaphoricity determination modules.
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The learned patterns are then normalized and applied to the corpus.
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It is probably the first analysis of Arabic parsing of this kind.
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We retain segmentation markersâwhich are consistent only in the vocalized section of the treebankâto differentiate between e.g. � âtheyâ and � + âtheir.â Because we use the vocalized section, we must remove null pronoun markers.
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The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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63 95.
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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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(3)), with one term for each classifier.
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The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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The model was built with open vocabulary, modified Kneser-Ney smoothing, and default pruning settings that remove singletons of order 3 and higher.
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In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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G1 and G2 are hanzi, we can estimate the probability of the sequence being a name as the product of: ⢠the probability that a word chosen randomly from a text will be a name-p(rule 1), and ⢠the probability that the name is of the form 1hanzi-family 2hanzi-given-p(rule 2), and ⢠the probability that the family name is the particular hanzi F1-p(rule 6), and ⢠the probability that the given name consists of the particular hanzi G1 and G2-p(rule 9) This model is essentially the one proposed in Chang et al.
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The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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A typical situation is shown in Figure 1.
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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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For the translation experiments, Eq. 2 is recursively evaluated.
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They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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3.3 Evaluation Results.
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This paper talks about Pseudo-Projective Dependency Parsing.
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The details of the transformation procedure are slightly different depending on the encoding schemes: d↑h let the linear head be the syntactic head). target arc must have the form wl −→ wm; if no target arc is found, Head is used as backoff. must have the form wl −→ wm and no outgoing arcs of the form wm p'↓ −→ wo; no backoff.
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In this paper the author evaluates machine translation performance for six European language pairs that participated in a shared task: translating French, German, Spanish texts to English and back.
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• We evaluated translation from English, in addition to into English.
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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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(f1; ;mg n fl1g ; l) 3 (f1; ;mg n fl; l1; l2g ; l0) !
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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.
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For example, the MCTAG shown in Figure 7 generates trees of the form shown in Figure 4b.
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These clusters are computed using an SVD variant without relying on transitional structure.
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5 70.1 58.
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The overall parsing accuracy obtained with the pseudo-projective approach is still lower than for the best projective parsers.
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In section 4 we evaluate these transformations with respect to projectivized dependency treebanks, and in section 5 they are applied to parser output.
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Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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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).
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They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines.
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Intuitively, it places more weight on OUT when less evidence from IN is available.
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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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On the MUC6 data, Bikel et al.
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Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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8 1 8.
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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, this result is consistent with the results of ex periments discussed in Wu and Fung (1994).
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Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.
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This would result in better rest cost estimation and better pruning.10 In general, tighter, but well factored, integration between the decoder and language model should produce a significant speed improvement.
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Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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The table shows that the lexicon tag frequency predicated by our full model are the closest to the gold standard.
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The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.
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We can make several observations on the cause of errors.
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It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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Purely statistical approaches have not been very popular, and so far as we are aware earlier work by Sproat and Shih (1990) is the only published instance of such an approach.
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This assumption, however, is not inherent to type-based tagging models.
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5 64.7 42.
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The PROBING data structure uses linear probing hash tables and is designed for speed.
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For RandLM, we used the settings in the documentation: 8 bits per value and false positive probability 1 256.
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Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.
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Later, BerkeleyLM (Pauls and Klein, 2011) described ideas similar to ours.
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They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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Our clue is the NE instance pairs.
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However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity.
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Since the early days of statistical NLP, researchers have observed that a part-of-speech tag distribution exhibits âone tag per discourseâ sparsity â words are likely to select a single predominant tag in a corpus, even when several tags are possible.
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It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure.
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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.
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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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We focus here instead on adapting the two most important features: the language model (LM), which estimates the probability p(wIh) of a target word w following an ngram h; and the translation models (TM) p(slt) and p(t1s), which give the probability of source phrase s translating to target phrase t, and vice versa.
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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.
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8 We use head-finding rules specified by a native speaker.
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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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The overall performance of the LTG system was outstanding, but the system consists of a sequence of many hand-coded rules and machine-learning modules.
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They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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We would like to thank Prof. Ralph Grish- man, Mr. Takaaki Hasegawa and Mr. Yusuke Shinyama for useful comments, discussion and evaluation.
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This paper talks about Pseudo-Projective Dependency Parsing.
| 0 |
With respect to exact match, the improvement is even more noticeable, which shows quite clearly that even if non-projective dependencies are rare on the token level, they are nevertheless important for getting the global syntactic structure correct.
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Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.
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We did have a problem classifying some modified noun phrases where the modified phrase does not represent a qualified or restricted form of the head, like âchairmanâ and âvice chairmanâ, as these are both represented by the keyword âchairmanâ.
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This paper presents methods to query N-gram language models, minimizing time and space costs.
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The PROBING model is 2.4 times as fast as the fastest alternative, SRILM, and uses less memory too.
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Combining multiple highly-accurate independent parsers yields promising results.
| 0 |
The maximum precision oracle is an upper bound on the possible gain we can achieve by parse hybridization.
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The approach has been successfully tested on the 8 000-word Verbmobil task.
| 0 |
The advantage is that we can recombine search hypotheses by dynamic programming.
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There is no global pruning.
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= p(fj je) max Ã;e00 j02Cnfjg np(jjj0; J) p(Ã) pÃ(eje0; e00) Qe00 (e0;C n fjg; j 0 )o: The DP equation is evaluated recursively for each hypothesis (e0; e; C; j).
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The PROBING data structure uses linear probing hash tables and is designed for speed.
| 0 |
TPT has theoretically better locality because it stores ngrams near their suffixes, thereby placing reads for a single query in the same or adjacent pages.
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Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be.
| 0 |
Phrase-level granularity distinguishes our work from previous work by Matsoukas et al (2009), who weight sentences according to sub-corpus and genre membership.
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Two general approaches are presented and two combination techniques are described for each approach.
| 0 |
Exploiting Diversity in Natural Language Processing: Combining Parsers
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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 |
For graph propagation, the hyperparameter v was set to 2 x 10−6 and was not tuned.
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This corpus has several advantages: it is annotated at different levels.
| 0 |
All commentaries have been tagged with part-of-speech information using Brantsâ TnT1 tagger and the Stuttgart/Tu¨bingen Tag Set automatic analysis was responsible for this decision.)
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However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity.
| 0 |
Statistics for all data sets are shown in Table 2.
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The approach assumes that the word reordering is restricted to a few positions in the source sentence.
| 0 |
Mai.
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Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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37.
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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) I f f fi * fi :1 }'l ij 1§: {1M m m s h e n 3 m e 0 shi2 ho u4 wo 3 cai2 ne ng 2 ke4 fu 2 zh e4 ge 4 ku n4 w h a t ti m e I just be abl e ov er co m e thi s C L dif fic 'When will I be able to overcome this difficulty?'
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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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5.1 Parsing Models.
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Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.
| 0 |
Section 4.1 explained that state s is stored by applications with partial hypotheses to determine when they can be recombined.
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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 |
Lattice parsing (Chappelier et al., 1999) is an alternative to a pipeline that prevents cascading errors by placing all segmentation options into the parse chart.
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It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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Formally, for a lexicon L and segments I â L, O â/ L, each word automaton accepts the language Iâ(O + I)Iâ.
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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 |
The results described in this paper suggest a characterization of mild context-sensitivity in terms of generalized context-freeness.
|
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 |
16 As one reviewer points out, one problem with the unigram model chosen here is that there is still a. tendency to pick a segmentation containing fewer words.
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the "Potsdam Commentary Corpus" or PCC consists of 170 commentaries from Ma¨rkische Allgemeine Zeitung, a German regional daily.
| 0 |
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.
|
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
| 0 |
In Section 6 we dis cuss other issues relating to how higher-order language models could be incorporated into the model.
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However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity.
| 0 |
5 70.1 58.
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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 |
Nu mb er filters candidate if number doesnât agree.
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The resulting model is compact, efficiently learnable and linguistically expressive.
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Both parameters depend on a single hyperparameter α.
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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.
| 0 |
The CoBoost algorithm described above divides the function Zco into two parts: Zco = 40 + 40.
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The approach has been successfully tested on the 8 000-word Verbmobil task.
| 0 |
Our approach uses word-to-word dependencies between source and target words.
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A beam search concept is applied as in speech recognition.
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Final (F): The rest of the sentence is processed monotonically taking account of the already covered positions.
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This corpus has several advantages: it is annotated at different levels.
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3.2 Stochastic rhetorical analysis.
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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 |
Ex: Mr. Cristiani, president of the country ...
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While many systems had similar performance, the results offered interesting insights, especially, about the relative performance of statistical and rule-based systems.
| 0 |
Adequacy (rank) Fluency (rank) BLEU (rank) upc-jmc (1-7) (1-8) (1-6) lcc (1-6) (1-7) (1-4) utd (1-7) (1-6) (2-7) upc-mr (1-8) (1-6) (1-7) nrc (1-7) (2-6) (8) ntt (1-8) (2-8) (1-7) cmu (3-7) (4-8) (2-7) rali (5-8) (3-9) (3-7) systran (9) (8-9) (10) upv (10) (10) (9) Spanish-English (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-jmc (1-7) (1-6) (1-5) ntt (1-7) (1-8) (1-5) lcc (1-8) (2-8) (1-4) utd (1-8) (2-7) (1-5) nrc (2-8) (1-9) (6) upc-mr (1-8) (1-6) (7) uedin-birch (1-8) (2-10) (8) rali (3-9) (3-9) (2-5) upc-jg (7-9) (6-9) (9) upv (10) (9-10) (10) German-English (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) uedin-phi (1-2) (1) (1) lcc (2-7) (2-7) (2) nrc (2-7) (2-6) (5-7) utd (3-7) (2-8) (3-4) ntt (2-9) (2-8) (3-4) upc-mr (3-9) (6-9) (8) rali (4-9) (3-9) (5-7) upc-jmc (2-9) (3-9) (5-7) systran (3-9) (3-9) (10) upv (10) (10) (9) Figure 7: Evaluation of translation to English on in-domain test data 112 English-French (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) nrc (1-5) (1-5) (1-6) upc-mr (1-4) (1-5) (1-6) upc-jmc (1-6) (1-6) (1-5) systran (2-7) (1-6) (7) utd (3-7) (3-7) (3-6) rali (1-7) (2-7) (1-6) ntt (4-7) (4-7) (1-5) English-Spanish (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) ms (1-5) (1-7) (7-8) upc-mr (1-4) (1-5) (1-4) utd (1-5) (1-6) (1-4) nrc (2-7) (1-6) (5-6) ntt (3-7) (1-6) (1-4) upc-jmc (2-7) (2-7) (1-4) rali (5-8) (6-8) (5-6) uedin-birch (6-9) (6-10) (7-8) upc-jg (9) (8-10) (9) upv (9-10) (8-10) (10) English-German (In Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-mr (1-3) (1-5) (3-5) ntt (1-5) (2-6) (1-3) upc-jmc (1-5) (1-4) (1-3) nrc (2-4) (1-5) (4-5) rali (3-6) (2-6) (1-4) systran (5-6) (3-6) (7) upv (7) (7) (6) Figure 8: Evaluation of translation from English on in-domain test data 113 French-English (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-jmc (1-5) (1-8) (1-4) cmu (1-8) (1-9) (4-7) systran (1-8) (1-7) (9) lcc (1-9) (1-9) (1-5) upc-mr (2-8) (1-7) (1-3) utd (1-9) (1-8) (3-7) ntt (3-9) (1-9) (3-7) nrc (3-8) (3-9) (3-7) rali (4-9) (5-9) (8) upv (10) (10) (10) Spanish-English (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-jmc (1-2) (1-6) (1-3) uedin-birch (1-7) (1-6) (5-8) nrc (2-8) (1-8) (5-7) ntt (2-7) (2-6) (3-4) upc-mr (2-8) (1-7) (5-8) lcc (4-9) (3-7) (1-4) utd (2-9) (2-8) (1-3) upc-jg (4-9) (7-9) (9) rali (4-9) (6-9) (6-8) upv (10) (10) (10) German-English (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) systran (1-4) (1-4) (7-9) uedin-phi (1-6) (1-7) (1) lcc (1-6) (1-7) (2-3) utd (2-7) (2-6) (4-6) ntt (1-9) (1-7) (3-5) nrc (3-8) (2-8) (7-8) upc-mr (4-8) (6-8) (4-6) upc-jmc (4-8) (3-9) (2-5) rali (8-9) (8-9) (8-9) upv (10) (10) (10) Figure 9: Evaluation of translation to English on out-of-domain test data 114 English-French (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) systran (1) (1) (1) upc-jmc (2-5) (2-4) (2-6) upc-mr (2-4) (2-4) (2-6) utd (2-6) (2-6) (7) rali (4-7) (5-7) (2-6) nrc (4-7) (4-7) (2-5) ntt (4-7) (4-7) (3-6) English-Spanish (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) upc-mr (1-3) (1-6) (1-2) ms (1-7) (1-8) (6-7) utd (2-6) (1-7) (3-5) nrc (1-6) (2-7) (3-5) upc-jmc (2-7) (1-6) (3-5) ntt (2-7) (1-7) (1-2) rali (6-8) (4-8) (6-8) uedin-birch (6-10) (5-9) (7-8) upc-jg (8-9) (9-10) (9) upv (9) (8-9) (10) English-German (Out of Domain) Adequacy (rank) Fluency (rank) BLEU (rank) systran (1) (1-2) (1-6) upc-mr (2-3) (1-3) (1-5) upc-jmc (2-3) (3-6) (1-6) rali (4-6) (4-6) (1-6) nrc (4-6) (2-6) (2-6) ntt (4-6) (3-5) (1-6) upv (7) (7) (7) Figure 10: Evaluation of translation from English on out-of-domain test data 115 French-English In domain Out of Domain Adequacy Adequacy 0.3 0.3 • 0.2 0.2 0.1 0.1 -0.0 -0.0 -0.1 -0.1 -0.2 -0.2 -0.3 -0.3 -0.4 -0.4 -0.5 -0.5 -0.6 -0.6 -0.7 -0.7 •upv -0.8 -0.8 21 22 23 24 25 26 27 28 29 30 31 15 16 17 18 19 20 21 22 •upv •systran upcntt • rali upc-jmc • cc Fluency 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 •upv -0.5 •systran •upv upc -jmc • Fluency 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 • • • td t cc upc- • rali 21 22 23 24 25 26 27 28 29 30 31 15 16 17 18 19 20 21 22 Figure 11: Correlation between manual and automatic scores for French-English 116 Spanish-English Figure 12: Correlation between manual and automatic scores for Spanish-English -0.3 -0.4 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 •upv -0.4 •upv -0.3 In Domain •upc-jg Adequacy 0.3 0.2 0.1 -0.0 -0.1 -0.2 Out of Domain •upc-jmc •nrc •ntt Adequacy upc-jmc • • •lcc • rali • •rali -0.7 -0.5 -0.6 •upv 23 24 25 26 27 28 29 30 31 32 19 20 21 22 23 24 25 26 27 28 • •rali Fluency 0.2 0.1 -0.0 -0.1 -0.2 ntt • upc-mr •lcc •utd •upc-jg •rali Fluency 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 •upc-jmc • uedin-birch -0.5 -0.5 •upv 23 24 25 26 27 28 29 30 31 32 19 20 21 22 23 24 25 26 27 28 117 In Domain Out of Domain Adequacy Adequacy German-English 15 16 17 18 19 20 21 22 23 24 25 26 27 0.4 0.3 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 lcc • upc-jmc •systran •upv Fluency •ula •upc-mr •lcc 15 16 17 18 19 20 21 22 23 24 25 26 27 0.4 0.3 0.2 0.1 -0.0 -0.1 -0.2 -0.3 -0.4 •systran •upv •uedin-phi -jmc •rali •systran -0.3 -0.4 -0.5 -0.6 •upv 12 13 14 15 16 17 18 19 20 0.4 0.3 0.2 0.1 -0.0 -0.1 -0.2 Fluency uedin-phi • • •utd •upc-jmc •upc-mr 0.4 •rali -0.3 -0.4 -0.5 •upv 12 13 14 15 16 17 18 19 20 0.3 0.2 0.1 -0.0 -0.1 -0.2 English-French In Domain Out of Domain Adequacy Adequacy .
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The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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Better Arabic Parsing: Baselines, Evaluations, and Analysis
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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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This has led previous workers to adopt ad hoc linear weighting schemes (Finch and Sumita, 2008; Foster and Kuhn, 2007; L¨u et al., 2007).
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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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(3)) to be defined over unlabeled as well as labeled instances.
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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.
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Since the early days of statistical NLP, researchers have observed that a part-of-speech tag distribution exhibits âone tag per discourseâ sparsity â words are likely to select a single predominant tag in a corpus, even when several tags are possible.
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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.
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Another way to interpret this is that less than 5% of the correct constituents are missing from the hypotheses generated by the union of the three parsers.
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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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Examining the word fidanzato for the “No LP” and “With LP” models is particularly instructive.
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