lguenth/backsum · Datasets at Hugging Face

source_text stringlengths 27 368	label int64 0 1	target_text stringlengths 1 5.38k
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	tai2du2 'Taiwan Independence.'
Their method did not assume any knowledge about the target language, making it applicable to a wide array of resource-poor languages.	0	Unsupervised learning approaches appear to be a natural solution to this problem, as they require only unannotated text for training models.
The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.	0	For each set, the phrases with bracketed frequencies are considered not paraphrases in the set.
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98).	0	(3)), with one term for each classifier.
The approach has been successfully tested on the 8 000-word Verbmobil task.	0	The following recursive equation is evaluated: Qe0 (e; S; C; j) = (2) = p(fj je) max Ã;e00 np(jjj0; J) p(Ã) pÃ(eje0; e00) max (S0;j0) (S0 ;Cnfjg;j0)!(S;C;j) j02Cnfjg Qe00 (e0; S0;C n fjg; j0)o: The search ends in the hypotheses (I; f1; ; Jg; j).
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.	0	In the graphs, system scores are indicated by a point, the confidence intervals by shaded areas around the point.
The fact that projective dependency parsers can never exactly reproduce the analyses found in non-projective tree banks is often neglected because of the relative scarcity of problematic constructions.	0	Experiments using data from the Prague Dependency Treebank show that the combined system can handle nonprojective constructions with a precision sufficient to yield a significant improvement in overall parsing accuracy.
The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.	0	All the sentences have been analyzed by our chunker and NE tag- ger.
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	We show that noun-noun vs. discourse-level coordination ambiguity in Arabic is a significant source of parsing errors (Table 8c).
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.	0	However, the learning curves in Figure 3 show that the Berkeley parser does not exceed our manual grammar by as wide a margin as has been shown for other languages (Petrov, 2009).
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.	0	Month Names, Days of the Week, and Numbers: If is initCaps and is one of January, February, . . .
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold.	0	At this step, we will try to link those sets, and put them into a single cluster.
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	This may be the sign of a maturing research environment.
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	But in most cases they can be used interchangably.
The approach assumes that the word reordering is restricted to a few positions in the source sentence.	0	The following auxiliary quantity is defined: Qe0 (e; C; j) := probability of the best partial hypothesis (ei 1; bi 1), where C = fbkjk = 1; ; ig, bi = j, ei = e and eiô1 = e0.
One can trivially create situations in which strictly binary-branching trees are combined to create a tree with only the root node and the terminal nodes, a completely flat structure.	0	As seen by the drop in average individual parser performance baseline, the introduced parser does not perform very well.
These clusters are computed using an SVD variant without relying on transitional structure.	0	Note that these observa sider suffix features, capitalization features, punctuation, and digit features.
They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines.	0	In this paper we have proposed an approach for instance-weighting phrase pairs in an out-of-domain corpus in order to improve in-domain performance.
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.	0	The easiest language pair according to BLEU (English-French: 28.33) received worse manual scores than the hardest (English-German: 14.01).
They have made use of local and global features to deal with the instances of same token in a document.	0	MUC7 has also seen hybrids of statistical NERs and hand-coded systems (Mikheev et al., 1998; Borthwick, 1999), notably Mikheev' s system, which achieved the best performance of 93.39% on the official NE test data.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.	0	computing the recall of the other's judgments relative to this standard.
This topic has been getting more attention, driven by the needs of various NLP applications.	0	The accuracy of the sets in representing paraphrase ranged from 73% to 99%, depending on the NE categories and set sizes; the accuracy of the links for two evaluated domains was 73% and 86%.
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.	0	58 95.
The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.	0	Recently, this topic has been getting more attention, as is evident from the Paraphrase Workshops in 2003 and 2004, driven by the needs of various NLP applications.
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 like to thank Ryan McDonald for numerous discussions on this topic.
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	Arabic is a morphologically rich language with a root-and-pattern system similar to other Semitic languages.
This topic has been getting more attention, driven by the needs of various NLP applications.	0	If there is a frequent multi-word sequence in a domain, we could use it as a keyword candidate.
Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be.	0	Section 2 describes our baseline techniques for SMT adaptation, and section 3 describes the instance-weighting approach.
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	We therefore used the arithmetic mean of each interjudge precision-recall pair as a single measure of interjudge similarity.
The overall parsing accuracy obtained with the pseudo-projective approach is still lower than for the best projective parsers.	0	In the second part of the experiment, we applied the inverse transformation based on breadth-first search under the three different encoding schemes.
Through parser combination we have reduced the precision error rate by 30% and the recall error rate by 6% compared to the best previously published result.	0	• Similarly, when the naïve Bayes classifier is configured such that the constituents require estimated probabilities strictly larger than 0.5 to be accepted, there is not enough probability mass remaining on crossing brackets for them to be included in the hypothesis.
This paper talks about KenLM: Faster and Smaller Language Model Queries.	0	Saving state allows our code to walk the data structure exactly once per query.
In this paper, the authors proposed an approach for instance-weighting phrase pairs in an out-of-domain corpus in order to improve in-domain performance.	0	Finally, we note that Jiang’s instance-weighting framework is broader than we have presented above, encompassing among other possibilities the use of unlabelled IN data, which is applicable to SMT settings where source-only IN corpora are available.
There is no global pruning.	0	For a given partial hypothesis (C; j), the order in which the cities in C have been visited can be ignored (except j), only the score for the best path reaching j has to be stored.
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	We trained this model by optimizing the following objective function: Note that this involves marginalizing out all possible state configurations z for a sentence x, resulting in a non-convex objective.
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.	0	For example, management succession systems must distinguish between a person who is fired and a person who is hired.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.	0	na me =>1 ha nzi fa mi ly 2 ha nzi gi ve n 3.
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	To show that the derivation trees of any grammar in LCFRS is a local set, we can rewrite the annotated derivation trees such that every node is labelled by a pair to include the composition operations.
The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.	0	We are not claiming that this method is almighty.
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	from the subset of the United Informatics corpus not used in the training of the models.
This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design.	0	markBaseNP indicates these non-recursive nominal phrases.
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.	0	For instance, the sentence Similar improvements in haemoglobin levels were reported in the scientific literature for other epoetins would likely be considered domain-specific despite the presence of general phrases like were reported in.
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.	0	hanzi in the various name positions, derived from a million names.
This paper talks about Unsupervised Models for Named Entity Classification.	0	The second algorithm builds on a boosting algorithm called AdaBoost (Freund and Schapire 97; Schapire and Singer 98).
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	For monolingual treebank data we relied on the CoNLL-X and CoNLL-2007 shared tasks on dependency parsing (Buchholz and Marsi, 2006; Nivre et al., 2007).
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.	0	37 79.
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.	0	Mutual information was shown to be useful in the segmentation task given that one does not have a dictionary.
Human judges also pointed out difficulties with the evaluation of long sentences.	0	In fact, it is very difficult to maintain consistent standards, on what (say) an adequacy judgement of 3 means even for a specific language pair.
The overall parsing accuracy obtained with the pseudo-projective approach is still lower than for the best projective parsers.	0	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.
The PROBING data structure uses linear probing hash tables and is designed for speed.	0	Compared to decoding, this task is cache-unfriendly in that repeated queries happen only as they naturally occur in text.
The corpus was annoted with different linguitic information.	0	Again, the idea is that having a picture of syntax, co-reference, and sentence-internal information structure at oneâs disposal should aid in finding models of discourse structure that are more explanatory and can be empirically supported.
BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results.	0	Section 4 presents experimen tal results on two corpora: the MUC4 terrorism corpus, and Reuters texts about natural disasters.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.	0	ogy (Koskenniemi 1983; Antworth 1990; Tzoukermann and Liberman 1990; Karttunen, Kaplan, and Zaenen 1992; Sproat 1992); we represent the fact that ir, attaches to nouns by allowing t:-transitions from the final states of all noun entries, to the initial state of the sub-WFST representing f,.
The fact that projective dependency parsers can never exactly reproduce the analyses found in non-projective tree banks is often neglected because of the relative scarcity of problematic constructions.	0	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.
The AdaBoost algorithm was developed for supervised learning.	0	Each xt E 2x is the set of features constituting the ith example.
It is probably the first analysis of Arabic parsing of this kind.	0	Contains digits.
The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text.	0	This flexibility, along with the simplicity of implementation and expansion, makes this framework an attractive base for continued research.
They proposed an unsupervised method to discover paraphrases from a large untagged corpus.	0	One of the difficulties in Natural Language Processing is the fact that there are many ways to express the same thing or event.
The contextual rules are restricted and may not be applicable to every example, but the spelling rules are generally applicable and should have good coverage.	0	The DL-CoTrain algorithm can be motivated as being a greedy method of satisfying the above 2 constraints.
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data.	0	For the manual scoring, we can distinguish only half of the systems, both in terms of fluency and adequacy.
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.	0	One possibility is to use n-grams based on mutual information.
It is probably the first analysis of Arabic parsing of this kind.	0	We start with noun features since written Arabic contains a very high proportion of NPs.
The AdaBoost algorithm was developed for supervised learning.	0	The unlabeled data gives many such "hints" that two features should predict the same label, and these hints turn out to be surprisingly useful when building a classifier.
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.	0	As we shall argue, the semantic class affiliation of a hanzi constitutes useful information in predicting its properties.
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs.	0	We used the MUC4 terrorism corpus (MUC4 Proceedings, 1992) and news articles from the Reuterâs text collection8 that had a subject code corresponding to natural disasters.
They believe that global context is useful in most languages, as it is a natural tendency for authors to use abbreviations on entities already mentioned previously.	0	of Articles No.
The manual evaluation of scoring translation on a graded scale from 1â5 seems to be very hard to perform.	0	By taking the ratio of matching n-grams to the total number of n-grams in the system output, we obtain the precision pn for each n-gram order n. These values for n-gram precision are combined into a BLEU score: The formula for the BLEU metric also includes a brevity penalty for too short output, which is based on the total number of words in the system output c and in the reference r. BLEU is sensitive to tokenization.
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.	0	Annotators suggested that long sentences are almost impossible to judge.
They believe that global context is useful in most languages, as it is a natural tendency for authors to use abbreviations on entities already mentioned previously.	0	Out-of-Vocabulary: We derived a lexicon list from WordNet 1.6, and words that are not found in this list have a feature out-of-vocabulary set to 1.
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.	1	By making use of global context, it has achieved excellent results on both MUC6 and MUC7 official test data.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.	0	Each trie node contains a sorted array of entries and they use binary search.
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.	0	64 94.
This topic has been getting more attention, driven by the needs of various NLP applications.	0	We would like to thank Prof. Ralph Grish- man, Mr. Takaaki Hasegawa and Mr. Yusuke Shinyama for useful comments, discussion and evaluation.
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	We can now compare this algorithm to that of (Yarowsky 95).
In this paper, the authors proposed an approach for instance-weighting phrase pairs in an out-of-domain corpus in order to improve in-domain performance.	0	However, for multinomial models like our LMs and TMs, there is a one to one correspondence between instances and features, eg the correspondence between a phrase pair (s, t) and its conditional multinomial probability p(s1t).
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	An additional case of super-segmental morphology is the case of Pronominal Clitics.
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data.	0	The confidence intervals are computed by bootstrap resampling for BLEU, and by standard significance testing for the manual scores, as described earlier in the paper.
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.	0	For statistics on this test set, refer to Figure 1.
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.	0	JA DE G O L D G R AS S SI C K NE SS DE AT H R A T 14.
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.	0	The system of Berg-Kirkpatrick et al.
On the basis of that observation, they described a class of formalisms which they called Linear Context- Free Rewriting Systems (LCFRs), and showed they were recognizable in polynomial time and generated only semilinear languages.	0	CFG's, TAG's, MCTAG's and HG's are all members of this class since they satisfy these restrictions.
Human judges also pointed out difficulties with the evaluation of long sentences.	0	The predominate focus of building systems that translate into English has ignored so far the difficult issues of generating rich morphology which may not be determined solely by local context.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.	0	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.
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.	0	In all figures, we present the per-sentence normalized judgements.
Their empirical results demonstrate that the type-based tagger rivals state-of-the-art tag-level taggers which employ more sophisticated learning mechanisms to exploit similar constraints.	0	t(i).
Human judges also pointed out difficulties with the evaluation of long sentences.	0	There is twice as much language modelling data, since training data for the machine translation system is filtered against sentences of length larger than 40 words.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.	0	We have argued that the proposed method performs well.
In this work, they take a more direct approach and treat a word type and its allowed POS tags as a primary element of the model.	0	The tokens w are generated by token-level tags t from an HMM parameterized by the lexicon structure.
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	We are especially grateful to Taylor Berg- Kirkpatrick for running additional experiments.
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data.	0	Figure 1 provides some statistics about this corpus.
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.	0	Figure 1: Translation of PCC sample commentary (STTS)2.
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs.	0	The scoping heuristics are based on the anaphor type: for reflexive pronouns the scope is the current clause, for relative pronouns it is the prior clause following its VP, for personal pronouns it is the anaphorâs sentence and two preceding sentences, and for definite NPs it is the anaphorâs sentence and eight preceding sentences.
They focused on phrases which two Named Entities, and proceed in two stages.	0	Step 1.
Here both parametric and non-parametric models are explored.	0	Adding the isolated constituents to our hypothesis parse could increase our expected recall, but in the cases we investigated it would invariably hurt our precision more than we would gain on recall.
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information.	0	Quite often, though, these directives fulfill the goal of increasing annotator agreement without in fact settling the theoretical question; i.e., the directives are clear but not always very well motivated.
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP).	0	On average, 6 reference translations per automatic translation are available.
It is probably the first analysis of Arabic parsing of this kind.	0	We extend the Stanford parser to accept pre-generated lattices, where each word is represented as a finite state automaton.
These clusters are computed using an SVD variant without relying on transitional structure.	0	Tag set As is standard, for all experiments, we set the number of latent model tag states to the size of the annotated tag set.
This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design.	0	The human evaluators were a non-native, fluent Arabic speaker (the first author) for the ATB and a native English speaker for the WSJ.7 Table 5 shows type- and token-level error rates for each corpus.
The PROBING data structure uses linear probing hash tables and is designed for speed.	0	However, TRIE partitions storage by n-gram length, so walking the trie reads N disjoint pages.

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

tai2du2 'Taiwan Independence.'

Their method did not assume any knowledge about the target language, making it applicable to a wide array of resource-poor languages.

0

Unsupervised learning approaches appear to be a natural solution to this problem, as they require only unannotated text for training models.

The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.

0

For each set, the phrases with bracketed frequencies are considered not paraphrases in the set.

The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98).

0

(3)), with one term for each classifier.

The approach has been successfully tested on the 8 000-word Verbmobil task.

0

The following recursive equation is evaluated: Qe0 (e; S; C; j) = (2) = p(fj je) max Ã;e00 np(jjj0; J) p(Ã) pÃ(eje0; e00) max (S0;j0) (S0 ;Cnfjg;j0)!(S;C;j) j02Cnfjg Qe00 (e0; S0;C n fjg; j0)o: The search ends in the hypotheses (I; f1; ; Jg; j).

Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.

0

In the graphs, system scores are indicated by a point, the confidence intervals by shaded areas around the point.

The fact that projective dependency parsers can never exactly reproduce the analyses found in non-projective tree banks is often neglected because of the relative scarcity of problematic constructions.

0

Experiments using data from the Prague Dependency Treebank show that the combined system can handle nonprojective constructions with a precision sufficient to yield a significant improvement in overall parsing accuracy.

The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.

0

All the sentences have been analyzed by our chunker and NE tag- ger.

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

We show that noun-noun vs. discourse-level coordination ambiguity in Arabic is a significant source of parsing errors (Table 8c).

The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.

0

However, the learning curves in Figure 3 show that the Berkeley parser does not exceed our manual grammar by as wide a margin as has been shown for other languages (Petrov, 2009).

The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.

0

Month Names, Days of the Week, and Numbers: If is initCaps and is one of January, February, . . .

In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold.

0

At this step, we will try to link those sets, and put them into a single cluster.

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

This may be the sign of a maturing research environment.

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

But in most cases they can be used interchangably.

The approach assumes that the word reordering is restricted to a few positions in the source sentence.

0

The following auxiliary quantity is defined: Qe0 (e; C; j) := probability of the best partial hypothesis (ei 1; bi 1), where C = fbkjk = 1; ; ig, bi = j, ei = e and eiô1 = e0.

One can trivially create situations in which strictly binary-branching trees are combined to create a tree with only the root node and the terminal nodes, a completely flat structure.

0

As seen by the drop in average individual parser performance baseline, the introduced parser does not perform very well.

These clusters are computed using an SVD variant without relying on transitional structure.

0

Note that these observa sider suffix features, capitalization features, punctuation, and digit features.

They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines.

0

In this paper we have proposed an approach for instance-weighting phrase pairs in an out-of-domain corpus in order to improve in-domain performance.

Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.

0

The easiest language pair according to BLEU (English-French: 28.33) received worse manual scores than the hardest (English-German: 14.01).

They have made use of local and global features to deal with the instances of same token in a document.

0

MUC7 has also seen hybrids of statistical NERs and hand-coded systems (Mikheev et al., 1998; Borthwick, 1999), notably Mikheev' s system, which achieved the best performance of 93.39% on the official NE test data.

In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.

0

computing the recall of the other's judgments relative to this standard.

This topic has been getting more attention, driven by the needs of various NLP applications.

0

The accuracy of the sets in representing paraphrase ranged from 73% to 99%, depending on the NE categories and set sizes; the accuracy of the links for two evaluated domains was 73% and 86%.

The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.

0

58 95.

The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.

0

Recently, this topic has been getting more attention, as is evident from the Paraphrase Workshops in 2003 and 2004, driven by the needs of various NLP applications.

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 like to thank Ryan McDonald for numerous discussions on this topic.

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

Arabic is a morphologically rich language with a root-and-pattern system similar to other Semitic languages.

This topic has been getting more attention, driven by the needs of various NLP applications.

0

If there is a frequent multi-word sequence in a domain, we could use it as a keyword candidate.

Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be.

0

Section 2 describes our baseline techniques for SMT adaptation, and section 3 describes the instance-weighting approach.

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

We therefore used the arithmetic mean of each interjudge precision-recall pair as a single measure of interjudge similarity.

The overall parsing accuracy obtained with the pseudo-projective approach is still lower than for the best projective parsers.

0

In the second part of the experiment, we applied the inverse transformation based on breadth-first search under the three different encoding schemes.

Through parser combination we have reduced the precision error rate by 30% and the recall error rate by 6% compared to the best previously published result.

0

• Similarly, when the naïve Bayes classifier is configured such that the constituents require estimated probabilities strictly larger than 0.5 to be accepted, there is not enough probability mass remaining on crossing brackets for them to be included in the hypothesis.

This paper talks about KenLM: Faster and Smaller Language Model Queries.

0

Saving state allows our code to walk the data structure exactly once per query.

In this paper, the authors proposed an approach for instance-weighting phrase pairs in an out-of-domain corpus in order to improve in-domain performance.

0

Finally, we note that Jiang’s instance-weighting framework is broader than we have presented above, encompassing among other possibilities the use of unlabelled IN data, which is applicable to SMT settings where source-only IN corpora are available.

There is no global pruning.

0

For a given partial hypothesis (C; j), the order in which the cities in C have been visited can be ignored (except j), only the score for the best path reaching j has to be stored.

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

We trained this model by optimizing the following objective function: Note that this involves marginalizing out all possible state configurations z for a sentence x, resulting in a non-convex objective.

However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.

0

For example, management succession systems must distinguish between a person who is fired and a person who is hired.

It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.

0

na me =>1 ha nzi fa mi ly 2 ha nzi gi ve n 3.

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

To show that the derivation trees of any grammar in LCFRS is a local set, we can rewrite the annotated derivation trees such that every node is labelled by a pair to include the composition operations.

The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.

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We are not claiming that this method is almighty.

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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from the subset of the United Informatics corpus not used in the training of the models.

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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markBaseNP indicates these non-recursive nominal phrases.

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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For instance, the sentence Similar improvements in haemoglobin levels were reported in the scientific literature for other epoetins would likely be considered domain-specific despite the presence of general phrases like were reported in.

Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.

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hanzi in the various name positions, derived from a million names.

This paper talks about Unsupervised Models for Named Entity Classification.

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The second algorithm builds on a boosting algorithm called AdaBoost (Freund and Schapire 97; Schapire and Singer 98).

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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For monolingual treebank data we relied on the CoNLL-X and CoNLL-2007 shared tasks on dependency parsing (Buchholz and Marsi, 2006; Nivre et al., 2007).

It is well-known that English constituency parsing models do not generalize to other languages and treebanks.

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37 79.

Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.

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Mutual information was shown to be useful in the segmentation task given that one does not have a dictionary.

Human judges also pointed out difficulties with the evaluation of long sentences.

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In fact, it is very difficult to maintain consistent standards, on what (say) an adequacy judgement of 3 means even for a specific language pair.

The overall parsing accuracy obtained with the pseudo-projective approach is still lower than for the best projective parsers.

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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.

The PROBING data structure uses linear probing hash tables and is designed for speed.

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Compared to decoding, this task is cache-unfriendly in that repeated queries happen only as they naturally occur in text.

The corpus was annoted with different linguitic information.

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Again, the idea is that having a picture of syntax, co-reference, and sentence-internal information structure at oneâs disposal should aid in finding models of discourse structure that are more explanatory and can be empirically supported.

BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results.

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Section 4 presents experimen tal results on two corpora: the MUC4 terrorism corpus, and Reuters texts about natural disasters.

It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.

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ogy (Koskenniemi 1983; Antworth 1990; Tzoukermann and Liberman 1990; Karttunen, Kaplan, and Zaenen 1992; Sproat 1992); we represent the fact that ir, attaches to nouns by allowing t:-transitions from the final states of all noun entries, to the initial state of the sub-WFST representing f,.

The fact that projective dependency parsers can never exactly reproduce the analyses found in non-projective tree banks is often neglected because of the relative scarcity of problematic constructions.

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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.

The AdaBoost algorithm was developed for supervised learning.

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Each xt E 2x is the set of features constituting the ith example.

It is probably the first analysis of Arabic parsing of this kind.

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Contains digits.

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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This flexibility, along with the simplicity of implementation and expansion, makes this framework an attractive base for continued research.

They proposed an unsupervised method to discover paraphrases from a large untagged corpus.

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One of the difficulties in Natural Language Processing is the fact that there are many ways to express the same thing or event.

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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The DL-CoTrain algorithm can be motivated as being a greedy method of satisfying the above 2 constraints.

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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For the manual scoring, we can distinguish only half of the systems, both in terms of fluency and adequacy.

Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.

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One possibility is to use n-grams based on mutual information.

It is probably the first analysis of Arabic parsing of this kind.

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We start with noun features since written Arabic contains a very high proportion of NPs.

The AdaBoost algorithm was developed for supervised learning.

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The unlabeled data gives many such "hints" that two features should predict the same label, and these hints turn out to be surprisingly useful when building a classifier.

they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.

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As we shall argue, the semantic class affiliation of a hanzi constitutes useful information in predicting its properties.

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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We used the MUC4 terrorism corpus (MUC4 Proceedings, 1992) and news articles from the Reuterâs text collection8 that had a subject code corresponding to natural disasters.

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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of Articles No.

The manual evaluation of scoring translation on a graded scale from 1â5 seems to be very hard to perform.

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By taking the ratio of matching n-grams to the total number of n-grams in the system output, we obtain the precision pn for each n-gram order n. These values for n-gram precision are combined into a BLEU score: The formula for the BLEU metric also includes a brevity penalty for too short output, which is based on the total number of words in the system output c and in the reference r. BLEU is sensitive to tokenization.

Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.

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Annotators suggested that long sentences are almost impossible to judge.

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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Out-of-Vocabulary: We derived a lexicon list from WordNet 1.6, and words that are not found in this list have a feature out-of-vocabulary set to 1.

The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.

1

By making use of global context, it has achieved excellent results on both MUC6 and MUC7 official test data.

The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.

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Each trie node contains a sorted array of entries and they use binary search.

The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.

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64 94.

This topic has been getting more attention, driven by the needs of various NLP applications.

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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.

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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We can now compare this algorithm to that of (Yarowsky 95).

In this paper, the authors proposed an approach for instance-weighting phrase pairs in an out-of-domain corpus in order to improve in-domain performance.

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However, for multinomial models like our LMs and TMs, there is a one to one correspondence between instances and features, eg the correspondence between a phrase pair (s, t) and its conditional multinomial probability p(s1t).

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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An additional case of super-segmental morphology is the case of Pronominal Clitics.

The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data.

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The confidence intervals are computed by bootstrap resampling for BLEU, and by standard significance testing for the manual scores, as described earlier in the paper.

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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For statistics on this test set, refer to Figure 1.

Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.

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JA DE G O L D G R AS S SI C K NE SS DE AT H R A T 14.

Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.

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The system of Berg-Kirkpatrick et al.

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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CFG's, TAG's, MCTAG's and HG's are all members of this class since they satisfy these restrictions.

Human judges also pointed out difficulties with the evaluation of long sentences.

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The predominate focus of building systems that translate into English has ignored so far the difficult issues of generating rich morphology which may not be determined solely by local context.

The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.

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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.

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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In all figures, we present the per-sentence normalized judgements.

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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t(i).

Human judges also pointed out difficulties with the evaluation of long sentences.

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There is twice as much language modelling data, since training data for the machine translation system is filtered against sentences of length larger than 40 words.

The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.

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We have argued that the proposed method performs well.

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.

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The tokens w are generated by token-level tags t from an HMM parameterized by the lexicon structure.

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.

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We are especially grateful to Taylor Berg- Kirkpatrick for running additional experiments.

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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Figure 1 provides some statistics about this corpus.

This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.

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Figure 1: Translation of PCC sample commentary (STTS)2.

Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs.

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The scoping heuristics are based on the anaphor type: for reflexive pronouns the scope is the current clause, for relative pronouns it is the prior clause following its VP, for personal pronouns it is the anaphorâs sentence and two preceding sentences, and for definite NPs it is the anaphorâs sentence and eight preceding sentences.

They focused on phrases which two Named Entities, and proceed in two stages.

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Step 1.

Here both parametric and non-parametric models are explored.

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Adding the isolated constituents to our hypothesis parse could increase our expected recall, but in the cases we investigated it would invariably hurt our precision more than we would gain on recall.

Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information.

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Quite often, though, these directives fulfill the goal of increasing annotator agreement without in fact settling the theoretical question; i.e., the directives are clear but not always very well motivated.

The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP).

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On average, 6 reference translations per automatic translation are available.

It is probably the first analysis of Arabic parsing of this kind.

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We extend the Stanford parser to accept pre-generated lattices, where each word is represented as a finite state automaton.

These clusters are computed using an SVD variant without relying on transitional structure.

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Tag set As is standard, for all experiments, we set the number of latent model tag states to the size of the annotated tag set.

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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The human evaluators were a non-native, fluent Arabic speaker (the first author) for the ATB and a native English speaker for the WSJ.7 Table 5 shows type- and token-level error rates for each corpus.

The PROBING data structure uses linear probing hash tables and is designed for speed.

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However, TRIE partitions storage by n-gram length, so walking the trie reads N disjoint pages.