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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. | 0 | The test accuracy more or less asymptotes. |
Instance-weighting approach improved over a wide range of baselines, giving gains of over 2 BLEU points over the best non-adapted baseline. | 0 | Although these authors report better gains than ours, they are with respect to a non-adapted baseline. |
In order to handle the necessary word reordering as an optimization problem within the dynamic programming approach, they describe a solution to the traveling salesman problem (TSP) which is based on dynamic programming. | 0 | Translation errors are reported in terms of multireference word error rate (mWER) and subjective sentence error rate (SSER). |
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 | Each token may admit multiple analyses, each of which a sequence of one or more lexemes (we use li to denote a lexeme) belonging a presupposed Hebrew lexicon LEX. |
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task. | 0 | = 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). |
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 | As mentioned above, it is not obvious how to apply Daum´e’s approach to multinomials, which do not have a mechanism for combining split features. |
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 | This decreases the statistical significance of our results compared to those studies. |
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs. | 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 . |
This paper presents a maximum entropy-based named entity recognizer (NER). | 0 | The local features used are similar to those used in BBN' s IdentiFinder (Bikel et al., 1999) or MENE (Borthwick, 1999). |
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs. | 0 | However, ince we extracted the test corpus automatically from web sources, the reference translation was not always accurate — due to sentence alignment errors, or because translators did not adhere to a strict sentence-by-sentence translation (say, using pronouns when referring to entities mentioned in the previous sentence). |
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university. | 0 | The wounds are still healing.), entity-attribute (e.g., She 2001), who determined that in their corpus of German computer tests, 38% of relations were lexically signalled. |
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information. | 0 | Then, moving from connective to connective, ConAno sometimes offers suggestions for its scope (using heuristics like âfor sub- junctor, mark all words up to the next comma as the first segmentâ), which the annotator can accept with a mouseclick or overwrite, marking instead the correct scope with the mouse. |
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data. | 0 | As far as we know, no other NERs have used information from the whole document (global) as well as information within the same sentence (local) in one framework. |
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 | Thus, TAG's can not give analyses in which dependencies between arbitrarily large branches exist. |
This corpus has several advantages: it is annotated at different levels. | 0 | Section 4 draws some conclusions from the present state of the effort. |
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data. | 0 | On the other hand, if it is seen as McCann Pte. |
The approach assumes that the word reordering is restricted to a few positions in the source sentence. | 0 | The details are given in (Och and Ney, 2000). |
In this work, they take a more direct approach and treat a word type and its allowed POS tags as a primary element of the model. | 0 | By design, they readily capture regularities at the token-level. |
The overall parsing accuracy obtained with the pseudo-projective approach is still lower than for the best projective parsers. | 0 | Finally, since non-projective constructions often involve long-distance dependencies, the problem is closely related to the recovery of empty categories and non-local dependencies in constituency-based parsing (Johnson, 2002; Dienes and Dubey, 2003; Jijkoun and de Rijke, 2004; Cahill et al., 2004; Levy and Manning, 2004; Campbell, 2004). |
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 | 4.3 Morphological Analysis. |
In order to handle the necessary word reordering as an optimization problem within the dynamic programming approach, they describe a solution to the traveling salesman problem (TSP) which is based on dynamic programming. | 0 | Again, the monotone search performs worst. |
A beam search concept is applied as in speech recognition. | 0 | For the inverted alignment probability p(bijbiô1; I; J), we drop the dependence on the target sentence length I. 2.2 Word Joining. |
They proposed an unsupervised method to discover paraphrases from a large untagged corpus. | 0 | If the same pair of NE instances is used with different phrases, these phrases are likely to be paraphrases. |
They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines. | 0 | Jiang and Zhai (2007) suggest the following derivation, making use of the true OUT distribution po(s, t): where each fi(s, t) is a feature intended to charac- !0ˆ = argmax pf(s, t) log pθ(s|t) (8) terize the usefulness of (s, t), weighted by Ai. θ s,t pf(s, t)po(s, t) log pθ(s|t) The mixing parameters and feature weights (col- != argmax po (s, t) lectively 0) are optimized simultaneously using dev- θ s,t pf(s, t)co(s, t) log pθ(s|t), set maximum likelihood as before: !�argmax po (s, t) ! θ s,t �ˆ = argmax ˜p(s, t) log p(s|t; 0). |
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low. | 0 | 96 75. |
They plan on extending instance-weighting to other standard SMT components and capture the degree of generality of phrase pairs. | 0 | The 14 general-language features embody straightforward cues: frequency, “centrality” as reflected in model scores, and lack of burstiness. |
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 | The work of the second author as well as collaboration visits to Israel was financed by NWO, grant number 017.001.271. |
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data. | 0 | The test set included 2000 sentences from the Europarl corpus, but also 1064 sentences out-ofdomain test data. |
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP). | 0 | mein 5. |
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information. | 0 | Clearly this poses a number of research challenges, though, such as the applicability of tag sets across different languages. |
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98). | 0 | We are currently exploring such algorithms. |
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 | Chinese word segmentation can be viewed as a stochastic transduction problem. |
In order to handle the necessary word reordering as an optimization problem within the dynamic programming approach, they describe a solution to the traveling salesman problem (TSP) which is based on dynamic programming. | 0 | The algorithm works due to the fact that not all permutations of cities have to be considered explicitly. |
Two general approaches are presented and two combination techniques are described for each approach. | 0 | Because we are working with only three parsers, the only situation in which context will help us is when it can indicate we should choose to believe a single parser that disagrees with the majority hypothesis instead of the majority hypothesis itself. |
They found replacing it with a ranked evaluation to be more suitable. | 0 | While we had up to 11 submissions for a translation direction, we did decide against presenting all 11 system outputs to the human judge. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | Jud ges A G G R ST M 1 M 2 M 3 T1 T2 T3 AG 0.7 0 0.7 0 0 . 4 3 0.4 2 0.6 0 0.6 0 0.6 2 0.5 9 GR 0.9 9 0 . 6 2 0.6 4 0.7 9 0.8 2 0.8 1 0.7 2 ST 0 . 6 4 0.6 7 0.8 0 0.8 4 0.8 2 0.7 4 M1 0.7 7 0.6 9 0.7 1 0.6 9 0.7 0 M2 0.7 2 0.7 3 0.7 1 0.7 0 M3 0.8 9 0.8 7 0.8 0 T1 0.8 8 0.8 2 T2 0.7 8 respectively, the recall and precision. |
Due to many similarly performing systems, the author was not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics. | 0 | The normalized judgement per sentence is the raw judgement plus (0 minus average raw judgement for this judge on this sentence). |
There is no global pruning. | 0 | A modified language model probability pÃ(eje0; e00) is defined as follows: pÃ(eje0; e00) = 1:0 if à = 0 p(eje0; e00) if à = 1 : We associate a distribution p(Ã) with the two cases à = 0 and à = 1 and set p(à = 1) = 0:7. |
It is well-known that English constituency parsing models do not generalize to other languages and treebanks. | 0 | Evalb is a Java re-implementation of the standard labeled precision/recall metric.12 The ATB gives all punctuation a single tag. |
They proposed a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity. | 0 | The work of the second author as well as collaboration visits to Israel was financed by NWO, grant number 017.001.271. |
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data. | 0 | It is well know that language pairs such as EnglishGerman pose more challenges to machine translation systems than language pairs such as FrenchEnglish. |
Their method did not assume any knowledge about the target language, making it applicable to a wide array of resource-poor languages. | 0 | Note that because we extracted only high-confidence alignments, many foreign vertices will not be connected to any English vertices. |
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information. | 0 | 3.2 Stochastic rhetorical analysis. |
They used graph-based label propagation for cross-lingual knowledge transfer and used the projected labels as features in an unsupervised model. | 0 | By aggregating the POS labels of the English tokens to types, we can generate label distributions for the English vertices. |
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. | 0 | For some language pairs (such as GermanEnglish) system performance is more divergent than for others (such as English-French), at least as measured by BLEU. |
Human judges also pointed out difficulties with the evaluation of long sentences. | 0 | This is less than the 694 judgements 2004 DARPA/NIST evaluation, or the 532 judgements in the 2005 DARPA/NIST evaluation. |
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform. | 0 | This revealed interesting clues about the properties of automatic and manual scoring. |
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse. | 0 | The contextual role knowledge that BABAR uses for coreference resolution is derived from this caseframe data. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | 19 We note that it is not always clear in Wang, Li, and Chang's examples which segmented words. |
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data. | 0 | In-domain Systran scores on this metric are lower than all statistical systems, even the ones that have much worse human scores. |
This paper talks about Pseudo-Projective Dependency Parsing. | 0 | More details on the parsing algorithm can be found in Nivre (2003). |
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers. | 0 | In the case of adverbial reduplication illustrated in (3b) an adjective of the form AB is reduplicated as AABB. |
Instance-weighting approach improved over a wide range of baselines, giving gains of over 2 BLEU points over the best non-adapted baseline. | 0 | In future work we plan to try this approach with more competitive SMT systems, and to extend instance weighting to other standard SMT components such as the LM, lexical phrase weights, and lexicalized distortion. |
This topic has been getting more attention, driven by the needs of various NLP applications. | 0 | For example, in Information Retrieval (IR), we have to match a userâs query to the expressions in the desired documents, while in Question Answering (QA), we have to find the answer to the userâs question even if the formulation of the answer in the document is different from the question. |
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 | As with personal names, we also derive an estimate from text of the probability of finding a transliterated name of any kind (PTN). |
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 | These two restrictions impose the constraint that the result of composing any two structures should be a structure whose "size" is the sum of its constituents plus some constant For example, the operation 4, discussed in the case of CFG's (in Section 4.1) adds the constant equal to the sum of the length of the strings VI, un+r• Since we are considering formalisms with arbitrary structures it is difficult to precisely specify all of the restrictions on the composition operations that we believe would appropriately generalize the concatenation operation for the particular structures used by the formalism. |
An extended lexicon model is defined, and its likelihood is compared to a baseline lexicon model, which takes only single-word dependencies into account. | 0 | For the error counts, a range from 0:0 to 1:0 is used. |
Across eight European languages, their approach results in an average absolute improvement of 10.4% over a state-of-the-art baseline, and 16.7% over vanilla hidden Markov models induced with the Expectation Maximization algorithm. | 0 | We would also like to thank Amarnag Subramanya for helping us with the implementation of label propagation and Shankar Kumar for access to the parallel data. |
We chose one of four labels for each example: location, person, organization, or noise where the noise category was used for items that were outside the three categories. | 0 | AdaBoost was first introduced in (Freund and Schapire 97); (Schapire and Singer 98) gave a generalization of AdaBoost which we will use in this paper. |
Finally, several coreference systems have successfully incorporated anaphoricity determination modules. | 0 | Since pronouns carry little semantics of their own, resolving them depends almost entirely on context. |
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration. | 0 | State is implemented in their scrolling variant, which is a trie annotated with forward and backward pointers. |
Instance-weighting approach improved over a wide range of baselines, giving gains of over 2 BLEU points over the best non-adapted baseline. | 0 | We have not explored this strategy. |
Explanations for this phenomenon are relative informativeness of lexicalization, insensitivity to morphology and the effect of variable word order and these factors lead to syntactic disambiguation. | 0 | Table 8b shows that verbal nouns are the hardest pre-terminal categories to identify. |
There are clustering approaches that assign a single POS tag to each word type. | 0 | (2010) consistently outperforms ours on English, we obtain substantial gains across other languages. |
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. | 0 | Manual and Automatic Evaluation of Machine Translation between European Languages |
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words. | 0 | An initial step of any text analysis task is the tokenization of the input into words. |
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 | The use of ILP in learning the desired grammar significantly increases the computational complexity of this method. |
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration. | 0 | This information is readily available in TRIE where adjacent records with equal pointers indicate no further extension of context is possible. |
They found replacing it with a ranked evaluation to be more suitable. | 0 | On the other hand, when all systems produce muddled output, but one is better, and one is worse, but not completely wrong, a judge is inclined to hand out judgements of 4, 3, and 2. |
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse. | 0 | 3 These are not full case frames in the traditional sense, but they approximate a simple case frame with a single slot. |
This paper conducted research in the area of automatic paraphrase discovery. | 0 | The results, along with the total number of phrases, are shown in Table 1. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | J:j:l :zhongl :0.0 ;m,Jlong4 :0.0 (mHHaryg9tltHBI) £: _ADV: 5.98 ¥ :hua2:o.o E :_NC: 4.41 :mln2:o.o mm : guo2 : 0.0 (RopubllcofChlna) .....,. |
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98). | 0 | In many cases, inspection of either the spelling or context alone is sufficient to classify an example. |
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity. | 0 | We experiment with four values for each hyperparameter resulting in 16 (α, β) combinations: α β 0.001, 0.01, 0.1, 1.0 0.01, 0.1, 1.0, 10 Iterations In each run, we performed 30 iterations of Gibbs sampling for the type assignment variables W .4 We use the final sample for evaluation. |
These clusters are computed using an SVD variant without relying on transitional structure. | 0 | Learned Tag Prior (PRIOR) We next assume there exists a single prior distribution Ï over tag assignments drawn from DIRICHLET(β, K ). |
It is probably the first analysis of Arabic parsing of this kind. | 0 | Third, we develop a human interpretable grammar that is competitive with a latent variable PCFG. |
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low. | 0 | 32 81. |
Vijay-Shankar et all considered the structural descriptions produced by various grammatical formalisms in terms of the complexity of the paths and the relationship between paths in the sets of structural descriptions that each system can generate. | 0 | In this paper, we outline how such family of formalisms can be defined, and show that like CFG's, each member possesses a number of desirable linguistic and computational properties: in particular, the constant growth property and polynomial recognizability. |
The corpus was annoted with different linguitic information. | 0 | annotation guidelines that tell annotators what to do in case of doubt. |
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure. | 0 | 2.1 Part-of-speech tags. |
They focused on phrases which two Named Entities, and proceed in two stages. | 0 | While there are other obstacles to completing this idea, we believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system. |
BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results. | 0 | Next, we describe four contextual role knowledge sources that are created from the training examples and the caseframes. |
Here we present two algorithms. | 0 | Before describing the unsupervised case we first describe the supervised version of the algorithm: Input to the learning algorithm: n labeled examples of the form (xi, y„). y, is the label of the ith example (given that there are k possible labels, y, is a member of y = {1 ... 0). xi is a set of mi features {x,1, Xi2 . |
An extended lexicon model is defined, and its likelihood is compared to a baseline lexicon model, which takes only single-word dependencies into account. | 0 | To explicitly handle the word reordering between words in source and target language, we use the concept of the so-called inverted alignments as given in (Ney et al., 2000). |
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems. | 0 | For each candidate antecedent, BABAR identifies the caseframe that would extract the candidate, pairs it with the anaphorâs caseframe, and consults the CF Network to see if this pair of caseframes has co-occurred in previous resolutions. |
The AdaBoost algorithm was developed for supervised learning. | 0 | Supervised methods have been applied quite successfully to the full MUC named-entity task (Bikel et al. 97). |
The texts were annotated with the RSTtool. | 0 | (Brandt 1996) extended these ideas toward a conception of kommunikative Gewichtung (âcommunicative-weight assignmentâ). |
Replacing this with a ranked evaluation seems to be more suitable. | 0 | This is because different judges focused on different language pairs. |
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 | Clearly, for judges h and h taking h as standard and computing the precision and recall for Jz yields the same results as taking h as the standard, and computing for h, 14 All evaluation materials, with the exception of those used for evaluating personal names were drawn. |
Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be. | 0 | An alternate approximation to (8) would be to let w,\(s, t) directly approximate pˆI(s, t). |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | The major problem for all segmentation systems remains the coverage afforded by the dictionary and the lexical rules used to augment the dictionary to deal with unseen words. |
They proposed an unsupervised method to discover paraphrases from a large untagged corpus. | 0 | Also, expanding on the techniques for the automatic generation of extraction patterns (Riloff 96; Sudo 03) using our method, the extraction patterns which have the same meaning can be automatically linked, enabling us to produce the final table fully automatically. |
In this work, they take a more direct approach and treat a word type and its allowed POS tags as a primary element of the model. | 0 | For inference, we are interested in the posterior probability over the latent variables in our model. |
It is well-known that English constituency parsing models do not generalize to other languages and treebanks. | 0 | Modifying the Berkeley parser for Arabic is straightforward. |
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold. | 0 | Find keywords for each NE pair The keywords are found for each NE category pair. |
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 | This step ensures that the most frequent terms for each domain are labeled (in case some of them are not in WordNet) and labeled with the sense most appropriate for the domain. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | Examples will usually be accompanied by a translation, plus a morpheme-by-morpheme gloss given in parentheses whenever the translation does not adequately serve this purpose. |
Using less training data than other systems, their NER can perform as well as other state-of-the-art NERs. | 0 | A secondary reference resolution classifier has information on the class assigned by the primary classifier. |
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 | Full Chinese personal names are in one respect simple: they are always of the form family+given. |
The main result is that the combined system can recover non-projective dependencies with a precision sufficient to give a significant improvement in overall parsing accuracy, especially with respect to the exact match criterion, leading to the best reported performance for robust non-projective parsing of Czech. | 0 | The second main result is that the pseudo-projective approach to parsing (using special arc labels to guide an inverse transformation) gives a further improvement of about one percentage point on attachment score. |
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