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Their empirical results demonstrate that the type-based tagger rivals state-of-the-art tag-level taggers which employ more sophisticated learning mechanisms to exploit similar constraints. | 0 | Instead, we condition on the type-level tag assignments T . Specifically, let St = {i|Ti = t} denote the indices of theword types which have been assigned tag t accord ing to the tag assignments T . Then θt is drawn from DIRICHLET(α, St), a symmetric Dirichlet which only places mass on word types indicated by St. This ensures that each word will only be assigned a single tag at inference time (see Section 4). |
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 | Because there might be some controversy about the exact definitions of such universals, this set of coarse-grained POS categories is defined operationally, by collapsing language (or treebank) specific distinctions to a set of categories that exists across all languages. |
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 | This enables us to build a high performance NER without using separate classifiers to take care of global consistency or complex formulation on smoothing and backoff models (Bikel et al., 1997). |
It is well-known that English constituency parsing models do not generalize to other languages and treebanks. | 0 | 3.2 Inter-annotator Agreement. |
It is probably the first analysis of Arabic parsing of this kind. | 0 | more frequently than is done in English. |
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 | Again, we can compute average scores for all systems for the different language pairs (Figure 6). |
The resulting model is compact, efficiently learnable and linguistically expressive. | 0 | Part-of-speech (POS) tag distributions are known to exhibit sparsity â a word is likely to take a single predominant tag in a corpus. |
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 | Unsupervised Learning of Contextual Role Knowledge for Coreference Resolution |
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 Bikel GoldPOS configuration only supplies the gold POS tags; it does not force the parser to use them. |
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity. | 0 | Even without features, but still using the tag prior, our median result is 52.0%, still significantly outperforming Grac¸a et al. |
Human judges also pointed out difficulties with the evaluation of long sentences. | 0 | Different sentence structure and rich target language morphology are two reasons for this. |
They have made use of local and global features to deal with the instances of same token in a document. | 0 | The most frequently occurring last words of organization names in cslist are compiled into a list of corporate suffixes, Corporate-Suffix- List. |
The corpus was annoted with different linguitic information. | 0 | The implementation is In a similar effort, (G¨otze 2003) developed a proposal for the theory-neutral annotation of information structure (IS) â a notoriously difficult area with plenty of conflicting and overlapping terminological conceptions. |
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy. | 0 | The BLEU score has been shown to correlate well with human judgement, when statistical machine translation systems are compared (Doddington, 2002; Przybocki, 2004; Li, 2005). |
The approach assumes that the word reordering is restricted to a few positions in the source sentence. | 0 | The search starts in the hypothesis (I; f;g; 0). |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | Minimal perfect hashing is used to find the index at which a quantized probability and possibly backoff are stored. |
Human judges also pointed out difficulties with the evaluation of long sentences. | 0 | This work was supported in part under the GALE program of the Defense Advanced Research Projects Agency, Contract No. |
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse. | 0 | We do not attempt to identify the types of relationships that are found. |
There are clustering approaches that assign a single POS tag to each word type. | 0 | .., Wn ) (obs) P T : Tag assigns (T1 ,. |
The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system. | 0 | The NE tagger is a rule-based system with 140 NE categories [Sekine et al. 2004]. |
This paper talks about KenLM: Faster and Smaller Language Model Queries. | 0 | Based on revision 4041, we modified Moses to print process statistics before terminating. |
This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers. | 0 | The combining algorithm is presented with the candidate parses and asked to choose which one is best. |
They have made use of local and global features to deal with the instances of same token in a document. | 0 | This process is repeated 5 times by rotating the data appropriately. |
This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers. | 0 | In Table 1 we see with very few exceptions that the isolated constituent precision is less than 0.5 when we use the constituent label as a feature. |
They focused on phrases which two Named Entities, and proceed in two stages. | 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. |
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university. | 0 | It is difficult to motivate these days why one ministry should be exempt from cutbacks â at the expense of the others. |
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 | Each xt E 2x is the set of features constituting the ith example. |
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 P (W |T , Ï) term in the lexicon component now decomposes as: n P (W |T , Ï) = n P (Wi|Ti, Ï) i=1 n   tions are not modeled by the standard HMM, which = n ï£ n P (v|ÏTi f ) instead can model token-level frequency. |
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data. | 0 | If one system is better in 95% of the sample sets, we conclude that its higher BLEU score is statistically significantly better. |
It outperformed strong unsupervised baselines as well as approaches that relied on direct projections, and bridged the gap between purely supervised and unsupervised POS tagging models. | 0 | We then extract a set of possible tags tx(y) by eliminating labels whose probability is below a threshold value τ: We describe how we choose τ in §6.4. |
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 | As in the case of the derivation trees of CFG's, nodes are labeled by a member of some finite set of symbols (perhaps only implicit in the grammar as in TAG's) used to denote derived structures. |
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 | The Penn Arabic Treebank (ATB) syntactic guidelines (Maamouri et al., 2004) were purposefully borrowed without major modification from English (Marcus et al., 1993). |
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 smooth guarantees that there are no zeroes estimated. |
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task. | 0 | The monotone search performs worst in terms of both error rates mWER and SSER. |
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 | As can be seen, GR and this "pared-down" statistical method perform quite similarly, though the statistical method is still slightly better.16 AG clearly performs much less like humans than these methods, whereas the full statistical algorithm, including morphological derivatives and names, performs most closely to humans among the automatic methods. |
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 | While automatic measures are an invaluable tool for the day-to-day development of machine translation systems, they are only a imperfect substitute for human assessment of translation quality, or as the acronym BLEU puts it, a bilingual evaluation understudy. |
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 | 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. |
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure. | 0 | Text generation, or at least the two phases of text planning and sentence planning, is a process driven partly by well-motivated choices (e.g., use this lexeme X rather than that more colloquial near-synonym Y ) and partly by con tation like that of PCC can be exploited to look for correlations in particular between syntactic structure, choice of referring expressions, and sentence-internal information structure. |
This paper conducted research in the area of automatic paraphrase discovery. | 0 | Find keywords for each NE pair The keywords are found for each NE category pair. |
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data. | 0 | All systems (except for Systran, which was not tuned to Europarl) did considerably worse on outof-domain training data. |
Finally, several coreference systems have successfully incorporated anaphoricity determination modules. | 0 | The goal of our research was to explore the use of contextual role knowledge for coreference resolution. |
Human judges also pointed out difficulties with the evaluation of long sentences. | 0 | To lower the barrier of entrance to the competition, we provided a complete baseline MT system, along with data resources. |
BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results. | 0 | For each domain, we created a blind test set by manually annotating 40 doc uments with anaphoric chains, which represent sets of m3 (S) = ) X â©Y =S 1 â ) m1 (X ) â m2 (Y ) m1 (X ) â m2 (Y ) (1) noun phrases that are coreferent (as done for MUC6 (MUC6 Proceedings, 1995)). |
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance. | 0 | The most popular approach to dealing with seg mentation ambiguities is the maximum matching method, possibly augmented with further heuristics. |
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold. | 0 | The procedure using the tagged sentences to discover paraphrases takes about one hour on a 2GHz Pentium 4 PC with 1GB of memory. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | Given names are most commonly two hanzi long, occasionally one hanzi long: there are thus four possible name types, which can be described by a simple set of context-free rewrite rules such as the following: 1. |
Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be. | 0 | For the LM, adaptive weights are set as follows: where α is a weight vector containing an element αi for each domain (just IN and OUT in our case), pi are the corresponding domain-specific models, and ˜p(w, h) is an empirical distribution from a targetlanguage training corpus—we used the IN dev set for this. |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | Both implementations employ a state object, opaque to the application, that carries information from one query to the next; we discuss both further in Section 4.2. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | A non-optimal analysis is shown with dotted lines in the bottom frame. |
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 | Each of the constituents must have received at least 1 votes from the k parsers, so a > I1 and 2 — 2k±-1 b > ri-5-111. |
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 | Raj and Whittaker (2003) show that integers in a trie implementation can be compressed substantially. |
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers. | 0 | The use of the Good-Turing equation presumes suitable estimates of the unknown expectations it requires. |
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 | Schapire and Singer show that the training error is bounded above by Thus, in order to greedily minimize an upper bound on training error, on each iteration we should search for the weak hypothesis ht and the weight at that minimize Z. |
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 | VBD she added VP PUNC â SBAR IN NP 0 NN. |
This topic has been getting more attention, driven by the needs of various NLP applications. | 0 | Limitations There are several limitations in the methods. |
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks. | 0 | Step 2. |
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 | The proof is given in (Tillmann, 2000). |
A beam search concept is applied as in speech recognition. | 0 | This algorithm can be applied to statistical machine translation. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | Methods for expanding the dictionary include, of course, morphological rules, rules for segmenting personal names, as well as numeral sequences, expressions for dates, and so forth (Chen and Liu 1992; Wang, Li, and Chang 1992; Chang and Chen 1993; Nie, Jin, and Hannan 1994). |
The AdaBoost algorithm was developed for supervised learning. | 0 | This modification brings the method closer to the DL-CoTrain algorithm described earlier, and is motivated by the intuition that all three labels should be kept healthily populated in the unlabeled examples, preventing one label from dominating — this deserves more theoretical investigation. |
This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design. | 0 | How should the absence of vowels and syntactic markers influence annotation choices and grammar development? |
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 | The performance of our system on those sentences ap peared rather better than theirs. |
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 | The composition operations in the case of CFG's are parameterized by the productions. |
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 | In our situation, the competing hypotheses are the possible antecedents for an anaphor. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | Particular relations are also consistent with particular hypotheses about the segmentation of a given sentence, and the scores for particular relations can be incremented or decremented depending upon whether the segmentations with which they are consistent are "popular" or not. |
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 | At each iteration the algorithm increases the number of rules, while maintaining a high level of agreement between the spelling and contextual decision lists. |
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 | Besides size of training data, the use of dictionaries is another factor that might affect performance. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | 4.3 Morphological Analysis. |
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 | 6One of our experimental settings lacks document boundaries, and we used this approximation in both settings for consistency. |
The manual evaluation of scoring translation on a graded scale from 1â5 seems to be very hard to perform. | 0 | • We evaluated translation from English, in addition to into English. |
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 | The only way to handle such phenomena within the framework described here is simply to expand out the reduplicated forms beforehand, and incorporate the expanded forms into the lexical transducer. |
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 statistical systems seem to still lag behind the commercial rule-based competition when translating into morphological rich languages, as demonstrated by the results for English-German and English-French. |
This paper presents methods to query N-gram language models, minimizing time and space costs. | 0 | IRST is not threadsafe. |
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 | At the same time, the n-gram error rate is sensitive to samples with extreme n-gram counts. |
The PROBING data structure uses linear probing hash tables and is designed for speed. | 0 | The 1-bit sign is almost always negative and the 8-bit exponent is not fully used on the range of values, so in practice this corresponds to quantization ranging from 17 to 20 total bits. |
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 | However, there are several reasons why this approach will not in general work: 1. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | While Gan's system incorporates fairly sophisticated models of various linguistic information, it has the drawback that it has only been tested with a very small lexicon (a few hundred words) and on a very small test set (thirty sentences); there is therefore serious concern as to whether the methods that he discusses are scalable. |
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task. | 0 | besuchen 9. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | This Good Turing estimate of p(unseen(f,) If,) can then be used in the normal way to define the probability of finding a novel instance of a construction in ir, in a text: p(unseen(f,)) = p(unseen(f,) I f,) p(fn Here p(ir,) is just the probability of any construction in ft as estimated from the frequency of such constructions in the corpus. |
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university. | 0 | For effectively annotating connectives/scopes, we found that existing annotation tools were not well-suited, for two reasons: ⢠Some tools are dedicated to modes of annotation (e.g., tiers), which could only quite un-intuitively be used for connectives and scopes. |
The second algorithm builds on a boosting algorithm called AdaBoost. | 0 | Limitations of (Blum and Mitchell 98): While the assumptions of (Blum and Mitchell 98) are useful in developing both theoretical results and an intuition for the problem, the assumptions are quite limited. |
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process. | 0 | Instead, we condition on the type-level tag assignments T . Specifically, let St = {i|Ti = t} denote the indices of theword types which have been assigned tag t accord ing to the tag assignments T . Then θt is drawn from DIRICHLET(α, St), a symmetric Dirichlet which only places mass on word types indicated by St. This ensures that each word will only be assigned a single tag at inference time (see Section 4). |
The departure from the traditional token-based tagging approach allow them to explicitly capture type-level distributional properties of valid POS tag assignments as part of the model. | 0 | 2 62.6 45. |
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy. | 0 | We asked participants to each judge 200–300 sentences in terms of fluency and adequacy, the most commonly used manual evaluation metrics. |
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems. | 0 | Table 3 shows BABARâs performance when the four contextual role knowledge sources are added. |
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 | To date we have not done a separate evaluation of foreign-name recognition. |
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 row represents the average accuracy of the three parsers we combine. |
The three parsers were trained and tuned by their creators on various sections of the WSJ portion of the Penn Treebank. | 0 | We then show that the combining techniques presented above give better parsing accuracy than any of the individual parsers. |
Using less training data than other systems, their NER can perform as well as other state-of-the-art NERs. | 0 | The effect of UNIQ is very small on both data sets. |
It is well-known that English constituency parsing models do not generalize to other languages and treebanks. | 0 | But diacritics are not present in unvocalized text, which is the standard form of, e.g., news media documents.3 VBD she added VP PUNC S VP VBP NP ... |
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity. | 0 | This approach makes the training objective more complex by adding linear constraints proportional to the number of word types, which is rather prohibitive. |
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 | (Charniak et al., 1996). |
Their method did not assume any knowledge about the target language, making it applicable to a wide array of resource-poor languages. | 0 | The function A : F —* C maps from the language specific fine-grained tagset F to the coarser universal tagset C and is described in detail in §6.2: Note that when tx(y) = 1 the feature value is 0 and has no effect on the model, while its value is −oc when tx(y) = 0 and constrains the HMM’s state space. |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | The model was built with open vocabulary, modified Kneser-Ney smoothing, and default pruning settings that remove singletons of order 3 and higher. |
The approach assumes that the word reordering is restricted to a few positions in the source sentence. | 0 | The advantage is that we can recombine search hypotheses by dynamic programming. |
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 dev corpus was taken from the NIST05 evaluation set, augmented with some randomly-selected material reserved from the training set. |
This paper talks about KenLM: Faster and Smaller Language Model Queries. | 0 | The compressed variant uses block compression and is rather slow as a result. |
There is no global pruning. | 0 | To be short, we omit the target words e; e0 in the formulation of the search hypotheses. |
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure. | 0 | This concerns on the one hand the basic question of retrieval, i.e. searching for information across the annotation layers (see 3.1). |
They found replacing it with a ranked evaluation to be more suitable. | 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. |
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