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they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement. | 0 | For derived words that occur in our corpus we can estimate these costs as we would the costs for an underived dictionary entry. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | (See also Wu and Fung [1994].) |
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 | We can give a tree pumping lemma for TAG's by adapting the uvwxy-theorem for CFL's since the tree sets of TAG's have independent and context-free paths. |
They showed the efficacy of graph-based label propagation for projecting part-of-speech information across languages. | 0 | While the first three models get three to four tags wrong, our best model gets only one word wrong and is the most accurate among the four models for this example. |
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 | 7). |
They showed the efficacy of graph-based label propagation for projecting part-of-speech information across languages. | 0 | Our “Projection” baseline is able to benefit from the bilingual information and greatly improves upon the monolingual baselines, but falls short of the “No LP” model by 2.5% on an average. |
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 | The weight on each sentence is a value in [0, 1] computed by a perceptron with Boolean features that indicate collection and genre membership. |
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 | This flexibility, along with the simplicity of implementation and expansion, makes this framework an attractive base for continued research. |
While many systems had similar performance, the results offered interesting insights, especially, about the relative performance of statistical and rule-based systems. | 0 | For the automatic evaluation, we used BLEU, since it is the most established metric in the field. |
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 | Each knowledge source then assigns a probability estimate to each candidate, which represents its belief that the candidate is the antecedent for the anaphor. |
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 | 4 69.0 51. |
A beam search concept is applied as in speech recognition. | 0 | A straightforward way to find the shortest tour is by trying all possible permutations of the n cities. |
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 | In fact, during the first rounds many of the predictions of Th., g2 are zero. |
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 | They considered a semi-supervised POS tagging scenario and showed that one can use a graph over trigram types, and edge weights based on distributional similarity, to improve a supervised conditional random field tagger. |
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 two new terms force the two classifiers to agree, as much as possible, on the unlabeled examples. |
The PROBING data structure uses linear probing hash tables and is designed for speed. | 0 | Queries take the form p(wn|wn−1 1 ) where wn1 is an n-gram. |
This paper talks about Pseudo-Projective Dependency Parsing. | 0 | On the other hand, we can expect Head+Path to be the most useful representation for reconstructing the underlying non-projective dependency graph. |
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information. | 0 | The corpus has been annotated with six different types of information, which are characterized in the following subsections. |
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 | The edge weights between the foreign language trigrams are computed using a co-occurence based similarity function, designed to indicate how syntactically similar the middle words of the connected trigrams are (§3.2). |
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 | θ has a belief value of 1.0, indicating complete certainty that the correct hypothesis is included in the set, and a plausibility value of 1.0, indicating that there is no evidence for competing hypotheses.5 As evidence is collected and the likely hypotheses are whittled down, belief is redistributed to subsets of θ. |
The resulting model is compact, efficiently learnable and linguistically expressive. | 0 | The extent to which this constraint is enforced varies greatly across existing methods. |
This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers. | 0 | The results in Table 2 were achieved on the development set. |
The features were weighted within a logistic model that gave an overall weight that was applied to the phrase pair and MAP-smoothed relative-frequency estimates which were combined linearly with relative-frequency estimates from an in-domain phrase table. | 0 | The corpus was wordaligned using both HMM and IBM2 models, and the phrase table was the union of phrases extracted from these separate alignments, with a length limit of 7. |
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 decision determines the inclusion or exclusion of a candidate constituent. |
The features were weighted within a logistic model that gave an overall weight that was applied to the phrase pair and MAP-smoothed relative-frequency estimates which were combined linearly with relative-frequency estimates from an in-domain phrase table. | 0 | We train linear mixture models for conditional phrase pair probabilities over IN and OUT so as to maximize the likelihood of an empirical joint phrase-pair distribution extracted from a development set. |
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration. | 0 | SRILM’s compact variant, IRSTLM, MITLM, and BerkeleyLM’s sorted variant are all based on this technique. |
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 | An error count of 0:0 is assigned to a perfect translation, and an error count of 1:0 is assigned to a semantically and syntactically wrong translation. |
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process. | 0 | The relevant variables are the set of token-level tags that appear before and after each instance of the ith word type; we denote these context pairs with the set {(tb, ta)} and they are contained in t(âi). |
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 | Evaluating parsing results in our joint framework, as argued by Tsarfaty (2006), is not trivial under the joint disambiguation task, as the hypothesized yield need not coincide with the correct one. |
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 relevant variables are the set of token-level tags that appear before and after each instance of the ith word type; we denote these context pairs with the set {(tb, ta)} and they are contained in t(âi). |
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 | This work was supported in part by the Swedish Research Council (621-2002-4207). |
All the texts were annotated by two people. | 0 | Annotation of syntactic structure for the core corpus has just begun. |
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance. | 0 | In this example there are four "input characters," A, B, C and D, and these map respectively to four "pronunciations" a, b, c and d. Furthermore, there are four "words" represented in the dictionary. |
This paper presents a maximum entropy-based named entity recognizer (NER). | 0 | We have used the Java-based opennlp maximum entropy package1. |
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 | When the maSdar lacks a determiner, the constituent as a whole resem bles the ubiquitous annexation construct � ?f iDafa. |
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 | Base NPs are the other significant category of nominal phrases. |
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 | Te rro ris m Na tur al Dis ast ers mu rde r of < NP > kill ed <p atie nt > <a ge nt > da ma ged wa s inj ure d in < NP > <a ge nt > rep ort ed <a ge nt > add ed <a ge nt > occ urr ed cau se of < NP > <a ge nt > stat ed <a ge nt > add ed <a ge nt > wr eak ed <a ge nt > cro sse d per pet rat ed <p atie nt > con de mn ed <p atie nt > dri ver of < NP > <a ge nt > car ryi ng Figure 1: Caseframe Network Examples Figure 1 shows examples of caseframes that co-occur in resolutions, both in the terrorism and natural disaster domains. |
We chose one of four labels for each example: location, person, organization, or noise where the noise category was used for items that were outside the three categories. | 0 | (2) was extended to have an additional, innermost loop over the (3) possible labels. |
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 | We used 22 features for the logistic weighting model, divided into two groups: one intended to reflect the degree to which a phrase pair belongs to general language, and one intended to capture similarity to the IN domain. |
Human judges also pointed out difficulties with the evaluation of long sentences. | 0 | This decreases the statistical significance of our results compared to those studies. |
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse. | 0 | In the natural disasters domain, agents are often forces of nature, such as hurricanes or wildfires. |
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 | 4 65.9 48. |
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university. | 0 | Still, for both human and automatic rhetorical analysis, connectives are the most important source of surface information. |
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task. | 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. |
Instance-weighting approach improved over a wide range of baselines, giving gains of over 2 BLEU points over the best non-adapted baseline. | 0 | Phrase-level granularity distinguishes our work from previous work by Matsoukas et al (2009), who weight sentences according to sub-corpus and genre membership. |
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 | We characterize a class of formalisms that have this property in Section 4. |
The PROBING data structure uses linear probing hash tables and is designed for speed. | 0 | When a collision occurs, linear probing places the entry to be inserted in the next (higher index) empty bucket, wrapping around as necessary. |
It is well-known that English constituency parsing models do not generalize to other languages and treebanks. | 0 | For Arabic we M o d e l S y s t e m L e n g t h L e a f A n c e s t o r Co rpu s Sent Exact E v a l b L P LR F1 T a g % B a s e l i n e 7 0 St an for d (v 1. |
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 | 6. 3) all G o l d P O S 7 0 0.7 91 0.825 358 0.7 73 0.818 358 0.8 02 0.836 452 80. |
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 | For a language like English, this problem is generally regarded as trivial since words are delimited in English text by whitespace or marks of punctuation. |
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure. | 0 | Finally, the focus/background partition is annotated, together with the focus question that elicits the corresponding answer. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | pronunciation depends upon word affiliation: tfJ is pronounced deO when it is a prenominal modification marker, but di4 in the word §tfJ mu4di4 'goal'; fl; is normally ganl 'dry,' but qian2 in a person's given name. |
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform. | 0 | A few pointed out that adequacy should be broken up into two criteria: (a) are all source words covered? |
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) and the posterior regular- ization HMM of Grac¸a et al. |
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks. | 0 | Smith estimates Lotus will make a profit this quarterâ¦â, our system extracts âSmith esti mates Lotusâ as an instance. |
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 final estimating equation is then: (3) Since the total of all these class estimates was about 10% off from the Turing estimate n1/N for the probability of all unseen hanzi, we renormalized the estimates so that they would sum to n 1jN. |
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 | Collisions between two keys in the table can be identified at model building time. |
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration. | 0 | Entries for 2 < n < N store a vocabulary identifier, probability, backoff, and an index into the n + 1-gram table. |
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 | Arabic sentences of up to length 63 would need to be. |
This paper talks about KenLM: Faster and Smaller Language Model Queries. | 0 | The developers suggested some changes, such as building the model from scratch with IRSTLM, but these did not resolve the problem. |
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 | These packages are further described in Section 3. |
A beam search concept is applied as in speech recognition. | 0 | The algorithm works due to the fact that not all permutations of cities have to be considered explicitly. |
They focused on phrases which two Named Entities, and proceed in two stages. | 0 | Smith estimates Lotus will make a profit this quarterâ¦â, our system extracts âSmith esti mates Lotusâ as an instance. |
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 | In the namedentity problem each example is a (spelling,context) pair. |
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 | However, in existing systems, this expansion come with a steep increase in model complexity. |
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP). | 0 | For the translation experiments, Eq. 2 is recursively evaluated. |
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers. | 0 | Particular instances of relations are associated with goodness scores. |
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 8 similarity-to-IN features are based on word frequencies and scores from various models trained on the IN corpus: To avoid numerical problems, each feature was normalized by subtracting its mean and dividing by its standard deviation. |
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP). | 0 | The negative logarithm of t0 is reported. |
Here we present two algorithms. | 0 | Denote the unthresholded classifiers after t — 1 rounds by git—1 and assume that it is the turn for the first classifier to be updated while the second one is kept fixed. |
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems. | 0 | 6 Our knowledge sources return some sort of probability estimate, although in some cases this estimate is not especially well-principled (e.g., the Recency KS). |
This paper talks about Pseudo-Projective Dependency Parsing. | 0 | When the parser is trained on the transformed data, it will ideally learn not only to construct projective dependency structures but also to assign arc labels that encode information about lifts. |
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 | Evaluation of the Segmentation as a Whole. |
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data. | 0 | Two lists, Corporate-Suffix-List (for corporate suffixes) and Person-Prefix-List (for person prefixes), are collected from the training data. |
Two general approaches are presented and two combination techniques are described for each approach. | 0 | For this reason, naïve Bayes classifiers are well-matched to this problem. |
These clusters are computed using an SVD variant without relying on transitional structure. | 0 | For inference, we are interested in the posterior probability over the latent variables in our model. |
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 first is an evaluation of the system's ability to mimic humans at the task of segmenting text into word-sized units; the second evaluates the proper-name identification; the third measures the performance on morphological analysis. |
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse. | 0 | The SynRole KS computes the relative frequency with which the candidatesâ syntactic role (subject, direct object, PP object) appeared in resolutions in the training 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 | Moreover, the Stanford parser achieves the most exact Leaf Ancestor matches and tagging accuracy that is only 0.1% below the Bikel model, which uses pre-tagged input. |
Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be. | 0 | Previous approaches have tried to find examples that are similar to the target domain. |
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks. | 0 | The number of NE instance pairs used in their experiment is less than half of our method. |
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 some language pairs (such as GermanEnglish) system performance is more divergent than for others (such as English-French), at least as measured by BLEU. |
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 | This alters generation of T as follows: n P (T |Ï) = n P (Ti|Ï) i=1 Note that this distribution captures the frequency of a tag across word types, as opposed to tokens. |
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process. | 0 | 5.1 Data Sets. |
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 | Arabic is a morphologically rich language with a root-and-pattern system similar to other Semitic languages. |
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 | 4 65.9 48. |
They proposed an unsupervised method to discover paraphrases from a large untagged corpus. | 0 | Extract NE instance pairs with contexts First, we extract NE pair instances with their context from the corpus. |
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 advantage is that we can recombine search hypotheses by dynamic programming. |
The PROBING data structure uses linear probing hash tables and is designed for speed. | 0 | Unlike Germann et al. (2009), we chose a model size so that all benchmarks fit comfortably in main memory. |
BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results. | 0 | A contextual role represents the role that a noun phrase plays in an event or relationship. |
This corpus has several advantages: it is annotated at different levels. | 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). |
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 | VBD she added VP PUNC â SBAR IN NP 0 NN. |
This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers. | 0 | The corpus-based statistical parsing community has many fast and accurate automated parsing systems, including systems produced by Collins (1997), Charniak (1997) and Ratnaparkhi (1997). |
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks. | 0 | buy - acquire (5) buy - agree (2) buy - purchase (5) buy - acquisition (7) buy - pay (2)* buy - buyout (3) buy - bid (2) acquire - purchase (2) acquire - acquisition (2) acquire - pay (2)* purchase - acquisition (4) purchase - stake (2)* acquisition - stake (2)* unit - subsidiary (2) unit - parent (5) It is clear that these links form two clusters which are mostly correct. |
They proposed a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity. | 0 | In our third model GTppp we also add the distinction between general PPs and possessive PPs following Goldberg and Elhadad (2007). |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | Other good classes include JADE and GOLD; other bad classes are DEATH and RAT. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | :zhong1 : 0.0 tjl :huo2 :0.0 (R:spub:/ic of Ch:ina) + .,_,...I : jlong4 :0.0 (mUifaty genG181) 0 £: _NC: 40.0 Figure 3 Partial Chinese Lexicon (NC = noun; NP = proper noun).c=- - I â¢=- :il: .;ss:;zhangt ⢠'-:. |
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance. | 0 | Since we could not bias the subjects towards a particular segmentation and did not presume linguistic sophistication on their part, the instructions were simple: subjects were to mark all places they might plausibly pause if they were reading the text aloud. |
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 | The authors provided us with a ratio between TPT and SRI under different conditions. aLossy compression with the same weights. bLossy compression with retuned weights. ditions make the value appropriate for estimating repeated run times, such as in parameter tuning. |
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 bias of automatic methods in favor of statistical systems seems to be less pronounced on out-of-domain test data. |
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