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This corpus has several advantages: it is annotated at different levels. | 0 | The portions of information in the large window can be individually clicked visible or invisible; here we have chosen to see (from top to bottom) ⢠the full text, ⢠the annotation values for the activated annotation set (co-reference), ⢠the actual annotation tiers, and ⢠the portion of text currently âin focusâ (which also appears underlined in the full text). |
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 | The general-language features have a slight advantage over the similarity features, and both are better than the SVM feature. |
From a DP-based solution to the traveling salesman problem, they present a novel technique to restrict the possible word reordering between source and target language in order to achieve an eÃcient search algorithm. | 0 | In Section 3, we introduce our novel concept to word reordering and a DP-based search, which is especially suitable for the translation direction from German to English. |
They focused on phrases which two Named Entities, and proceed in two stages. | 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. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | Their results are then compared with the results of an automatic segmenter. |
They used graph-based label propagation for cross-lingual knowledge transfer and used the projected labels as features in an unsupervised model. | 0 | We use label propagation in two stages to generate soft labels on all the vertices in the graph. |
A beam search concept is applied as in speech recognition. | 0 | Word Re-ordering and DP-based Search in Statistical Machine Translation |
They have made use of local and global features to deal with the instances of same token in a document. | 0 | We 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. |
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 each language, we took the same number of sentences from the bitext as there are in its treebank, and trained a supervised feature-HMM. |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | 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. |
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 | To date we have not done a separate evaluation of foreign-name recognition. |
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance. | 0 | Email: cls@bell-labs. |
The authors cluster NE instance pairs based on the words in the context using bag-of-words methods. | 0 | x EG, has agreed to be bought by H x EG, now owned by H x H to acquire EG x Hâs agreement to buy EG Three of those phrases are actually paraphrases, but sometime there could be some noise; such as the second phrase above. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | Examples are given in Table 4. |
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data. | 0 | The manual scores are averages over the raw unnormalized scores. |
This paper presents methods to query N-gram language models, minimizing time and space costs. | 0 | The BerkeleyLM direct-mapped cache is in principle faster than caches implemented by RandLM and by IRSTLM, so we may write a C++ equivalent implementation as future work. |
There are clustering approaches that assign a single POS tag to each word type. | 0 | For this experiment, we compare our model with the uniform tag assignment prior (1TW) with the learned prior (+PRIOR). |
the "Potsdam Commentary Corpus" or PCC consists of 170 commentaries from Ma¨rkische Allgemeine Zeitung, a German regional daily. | 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. |
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold. | 0 | All the NE pair instances which co-occur separated by at most 4 chunks are collected along with information about their NE types and the phrase between the NEs (the âcontextâ). |
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP). | 0 | In many cases, there is an even stronger restriction: over large portions of the source string, the alignment is monotone. |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | Sorted arrays store key-value pairs in an array sorted by key, incurring no space overhead. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | Virginia) and -sia are normally transliterated as fbSi! |
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 | Human evaluation is one way to distinguish between the two cases. |
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 | The first step in the learning process is to generate training examples consisting of anaphor/antecedent resolutions. |
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 | (2010), we adopt a simpler na¨ıve Bayes strategy, where all features are emitted independently. |
All the texts were annotated by two people. | 0 | On the other hand, we are interested in the application of rhetorical analysis or âdiscourse parsingâ (3.2 and 3.3), in text generation (3.4), and in exploiting the corpus for the development of improved models of discourse structure (3.5). |
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 | Training examples are generated automatically by identifying noun phrases that can be easily resolved with their antecedents using lexical and syntactic heuristics. |
Finally, several coreference systems have successfully incorporated anaphoricity determination modules. | 0 | 3 The Coreference Resolution Model. |
While many systems had similar performance, the results offered interesting insights, especially, about the relative performance of statistical and rule-based systems. | 0 | For more on the participating systems, please refer to the respective system description in the proceedings of the workshop. |
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. | 0 | Then we ran binary search to determine the least amount of memory with which it would run. |
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 | In previous work (Bean and Riloff, 1999), we developed an unsupervised learning algorithm that automatically recognizes definite NPs that are existential without syntactic modification because their meaning is universally understood. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | 17 They also provide a set of title-driven rules to identify names when they occur before titles such as $t. 1: xianlshengl 'Mr.' or i:l:itr!J tai2bei3 shi4zhang3 'Taipei Mayor.' |
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold. | 0 | For the experiments, we used four newswire corpora, the Los Angeles Times/Washington Post, The New York Times, Reuters and the Wall Street Journal, all published in 1995. |
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform. | 0 | While building a machine translation system is a serious undertaking, in future we hope to attract more newcomers to the field by keeping the barrier of entry as low as possible. |
It is probably the first analysis of Arabic parsing of this kind. | 0 | However, MADA is language-specific and relies on manually constructed dictionaries. |
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 | so that 'door' would be and in this case the hanzi 7C, does not represent a syllable. |
Their results suggested that it was possible to learn accurate POS taggers for languages which did not have any annotated data, but have translations into a resource-rich language. | 0 | For English POS tagging, BergKirkpatrick et al. (2010) found that this direct gradient method performed better (>7% absolute accuracy) than using a feature-enhanced modification of the Expectation-Maximization (EM) algorithm (Dempster et al., 1977).8 Moreover, this route of optimization outperformed a vanilla HMM trained with EM by 12%. |
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 also report word type level accuracy, the fraction of word types assigned their majority tag (where the mapping between model state and tag is determined by greedy one-to-one mapping discussed above).5 For each language, we aggregate results in the following way: First, for each hyperparameter setting, evaluate three variants: The first model (1TW) only 4 Typically, the performance stabilizes after only 10 itera-. |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | It also uses less memory, with 8 bytes of overhead per entry (we store 16-byte entries with m = 1.5); linked list implementations hash set and unordered require at least 8 bytes per entry for pointers. |
The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system. | 0 | Also, âagreeâ in the CC-domain is not a desirable keyword. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | It has been shown for English (Wang and Hirschberg 1992; Hirschberg 1993; Sproat 1994, inter alia) that grammatical part of speech provides useful information for these tasks. |
This topic has been getting more attention, driven by the needs of various NLP applications. | 0 | Evaluation results within sets Table 1 shows the evaluation result based on the number of phrases in a set. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | Since foreign names can be of any length, and since their original pronunciation is effectively unlimited, the identi fication of such names is tricky. |
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 | Therefore we opted to use the much 7 The DempsterShafer theory assumes that one of the hypotheses in θ is correct, so eliminating all of the hypotheses violates this assumption. |
The approach assumes that the word reordering is restricted to a few positions in the source sentence. | 0 | input: source string f1:::fj :::fJ initialization for each cardinality c = 1; 2; ; J do for each pair (C; j), where j 2 C and jCj = c do for each target word e 2 E Qe0 (e; C; j) = p(fj je) max Ã;e00 j02Cnfjg fp(jjj0; J) p(Ã) pÃ(eje0; e00) Qe00 (e0;C n fjg; j0)g words fj in the input string of length J. For the final translation each source position is considered exactly once. |
The authors cluster NE instance pairs based on the words in the context using bag-of-words methods. | 0 | The discovered paraphrases can be a big help to reduce human labor and create a more comprehensive pattern set. |
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words. | 0 | na me =>2 ha nzi fa mi ly 2 ha nzi gi ve n 5. |
The authors cluster NE instance pairs based on the words in the context using bag-of-words methods. | 0 | Finally, we find links between sets of phrases, based on the NE instance pair data (for example, different phrases which link âIBMâ and âLotusâ) (Step 4). |
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 | A high-level relation is agent, which relates an animate nominal to a predicate. |
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers. | 0 | Morphologically derived words such as, xue2shengl+men0. |
They have made use of local and global features to deal with the instances of same token in a document. | 0 | Context from the whole document can be important in classifying a named entity. |
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 DempsterShafer rule for combining pdfs is: to {C}, meaning that it is 70% sure the correct hypothesis is C. The intersection of these sets is the null set because these beliefs are contradictory. |
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 | Like verbs, maSdar takes arguments and assigns case to its objects, whereas it also demonstrates nominal characteristics by, e.g., taking determiners and heading iDafa (Fassi Fehri, 1993). |
They showed better grammars to improve performance on both morphological and syntactic tasks, providing support for the advantage of a joint framework over pipelined or factorized ones. | 0 | (we ignored the 419 trees in their development set.) |
The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system. | 0 | Here a set is represented by the keyword and the number in parentheses indicates the number of shared NE pair instances. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | Next, we represent the input sentence as an unweighted finite-state acceptor (FSA) I over H. Let us assume the existence of a function Id, which takes as input an FSA A, and produces as output a transducer that maps all and only the strings of symbols accepted by A to themselves (Kaplan and Kay 1994). |
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 | This is demonstrated by average scores over all systems, in terms of BLEU, fluency and adequacy, as displayed in Figure 5. |
Instance-weighting approach improved over a wide range of baselines, giving gains of over 2 BLEU points over the best non-adapted baseline. | 0 | The NIST06 and NIST08 evaluation sets were used for testing. |
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process. | 0 | Simply assigning to each word its most frequent associated tag in a corpus achieves 94.6% accuracy on the WSJ portion of the Penn Treebank. |
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 | However, TRIE partitions storage by n-gram length, so walking the trie reads N disjoint pages. |
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 | Besides the lack of a clear definition of what constitutes a correct segmentation for a given Chinese sentence, there is the more general issue that the test corpora used in these evaluations differ from system to system, so meaningful comparison between systems is rendered even more difficult. |
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 | The out-of-domain test set differs from the Europarl data in various ways. |
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 | This means that the rules in our grammar are of two kinds: (a) syntactic rules relating nonterminals to a sequence of non-terminals and/or PoS tags, and (b) lexical rules relating PoS tags to lattice arcs (lexemes). |
Human judges also pointed out difficulties with the evaluation of long sentences. | 0 | More judgements would have enabled us to make better distinctions, but it is not clear what the upper limit is. |
The AdaBoost algorithm was developed for supervised learning. | 0 | On each step CoBoost searches for a feature and a weight so as to minimize either 40 or 40. |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | We used this data to build an unpruned ARPA file with IRSTLM’s improved-kneser-ney option and the default three pieces. |
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low. | 0 | Unlike the WSJ corpus which has a high frequency of rules like VP âVB PP, Arabic verb phrases usually have lexi calized intervening nodes (e.g., NP subjects and direct objects). |
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 first author is supported by a National Defense Science and Engineering Graduate (NDSEG) fellowship. |
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP). | 0 | 1 is given in Fig. |
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 | Case and Zone of and : Similarly, if (or ) is initCaps, a feature (initCaps, zone) (or (initCaps, zone) ) is set to 1, etc. Token Information: This group consists of 10 features based on the string , as listed in Table 1. |
The approach assumes that the word reordering is restricted to a few positions in the source sentence. | 0 | In the following, we assume that this word joining has been carried out. |
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 distribution specifies the relative weight, or importance, of each example — typically, the weak learner will attempt to minimize the weighted error on the training set, where the distribution specifies the weights. |
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 | 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. |
This assumption, however, is not inherent to type-based tagging models. | 0 | 5 67.3 55. |
Combining multiple highly-accurate independent parsers yields promising results. | 0 | We would like to thank Eugene Charniak, Michael Collins, and Adwait Ratnaparkhi for enabling all of this research by providing us with their parsers and helpful comments. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | However, this result is consistent with the results of ex periments discussed in Wu and Fung (1994). |
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98). | 0 | Each round is composed of two stages; each stage updates one of the classifiers while keeping the other classifier fixed. |
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers. | 0 | Chang of Tsinghua University, Taiwan, R.O.C., for kindly providing us with the name corpora. |
There is no global pruning. | 0 | Each distance in the traveling salesman problem now corresponds to the negative logarithm of the product of the translation, alignment and language model probabilities. |
This architecture provides a uniform framework in which it is easy to incorporate not only listed dictionary entries but also morphological derivatives, and models for personal names and foreign names in transliteration. | 0 | However, it is almost universally the case that no clear definition of what constitutes a "correct" segmentation is given, so these performance measures are hard to evaluate. |
They employed a PCFG-based generative framework to make both syntactic and morphological disambiguation decisions which were not only theoretically clean and linguistically justified but also probabilistically appropriate and empirically sound. | 0 | We use double-circles to indicate the space-delimited token boundaries. |
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data. | 0 | At the very least, we are creating a data resource (the manual annotations) that may the basis of future research in evaluation metrics. |
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 | It was motivated by the observation that the (Yarowsky 95) algorithm added a very large number of rules in the first few iterations. |
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 | The first shows how constituent features and context do not help in deciding which parser to trust. |
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 | Sc opi ng filters candidate if outside the anaphorâs scope. |
This paper conducted research in the area of automatic paraphrase discovery. | 0 | Here, âEGâ represents âEastern Group Plcâ. |
Here both parametric and non-parametric models are explored. | 0 | If we were working with more than three parsers we could investigate minority constituents, those constituents that are suggested by at least one parser, but which the majority of the parsers do not suggest. |
There is no global pruning. | 0 | Input: Ja , wunderbar . Konnen wir machen . MonS: Yes, wonderful. |
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 | Second, we identified the 100 most frequent nouns in the training corpus and manually labeled them with semantic tags. |
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems. | 0 | If a candidate has a belief value ⥠.50, then we select that candidate as the antecedent for the anaphor. |
Combining multiple highly-accurate independent parsers yields promising results. | 0 | We include a constituent in our hypothesized parse if it appears in the output of a majority of the parsers. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | For E(ni1s), then, we substitute a smooth S against the number of class elements. |
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 | There are two possible reasons: (1) the knowledge sources are resolving different cases of anaphora, and (2) the knowledge sources provide multiple pieces of evidence in support of (or against) a candidate, thereby acting synergistically to push the DempsterShafer model over the belief threshold in favor of a single candidate. |
NER is useful in many NLP applications such as information extraction, question answering, etc .Chieu and Ng have shown that the maximum entropy framework is able to use global information directly from various sources. | 0 | A dynamic programming algorithm is then used to select the sequence of word classes with the highest probability. |
This paper talks about KenLM: Faster and Smaller Language Model Queries. | 0 | We reduce this to O(log log |A|) time by evenly distributing keys over their range then using interpolation search4 (Perl et al., 1978). |
This paper talks about Pseudo-Projective Dependency Parsing. | 0 | As long as the main evaluation metric is dependency accuracy per word, with state-of-the-art accuracy mostly below 90%, the penalty for not handling non-projective constructions is almost negligible. |
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data. | 0 | With the same Corporate- Suffix-List and Person-Prefix-List used in local features, for a token seen elsewhere in the same document with one of these suffixes (or prefixes), another feature Other-CS (or Other-PP) is set to 1. |
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 instance, for out-ofdomain English-French, Systran has the best BLEU and manual scores. |
Human judges also pointed out difficulties with the evaluation of long sentences. | 0 | There may be occasionally a system clearly at the top or at the bottom, but most systems are so close that it is hard to distinguish them. |
All the texts were annotated by two people. | 0 | In the rhetorical tree, nuclearity information is then used to extract a âkernel treeâ that supposedly represents the key information from which the summary can be generated (which in turn may involve co-reference information, as we want to avoid dangling pronouns in a summary). |
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