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lguenth
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backsum

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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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Interestingly, Chang et al. report 80.67% recall and 91.87% precision on an 11,000 word corpus: seemingly, our system finds as many names as their system, but with four times as many false hits.
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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We tokenize MWUs and their POS tags; this reduces the tag set size to 12.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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Email: cls@bell-labs.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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Papers that use this method or minor variants thereof include Liang (1986), Li et al.
The manual evaluation of scoring translation on a graded scale from 1–5 seems to be very hard to perform.
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We carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.
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.
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Hence, the terminal symbols appearing in the structures that are composed are not lost (though a constant number of new symbols may be introduced).
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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For each cell, the first row corresponds to the result using the best hyperparameter choice, where best is defined by the 11 metric.
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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A position is presented by the word at that position.
This paper talks about KenLM: Faster and Smaller Language Model Queries.
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Lossy compressed models RandLM (Talbot and Osborne, 2007) and Sheffield (Guthrie and Hepple, 2010) offer better memory consumption at the expense of CPU and accuracy.
A beam search concept is applied as in speech recognition.
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Table 3: Training and test conditions for the Verbmobil task (*number of words without punctuation marks).
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems.
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BABAR uses a DempsterShafer decision model (Stefik, 1995) to combine the evidence provided by the knowledge sources.
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98).
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AdaBoost.MH can be applied to the problem using these pseudolabels in place of supervised examples.
Using less training data than other systems, their NER can perform as well as other state-of-the-art NERs.
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4.1 Local Features.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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For a given "word" in the automatic segmentation, if at least k of the hu­ man judges agree that this is a word, then that word is considered to be correct.
This assumption, however, is not inherent to type-based tagging models.
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Statistics for all data sets are shown in Table 2.
This paper conducted research in the area of automatic paraphrase discovery.
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Limitations There are several limitations in the methods.
These clusters are computed using an SVD variant without relying on transitional structure.
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9 61.0 44.
The texts were annotated with the RSTtool.
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When finished, the whole material is written into an XML-structured annotation file.
The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.
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x EG, has agreed to be bought by H x EG, now owned by H x H to acquire EG x H’s agreement to buy EG Three of those phrases are actually paraphrases, but sometime there could be some noise; such as the second phrase above.
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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Compared to last year’s shared task, the participants represent more long-term research efforts.
The second algorithm builds on a boosting algorithm called AdaBoost.
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We excluded these from the evaluation as they can be easily identified with a list of days/months.
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP).
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The type of alignment we have considered so far requires the same length for source and target sentence, i.e. I = J. Evidently, this is an unrealistic assumption, therefore we extend the concept of inverted alignments as follows: When adding a new position to the coverage set C, we might generate either Æ = 0 or Æ = 1 new target words.
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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However, in order to capture the properties of various grammatical systems under consideration, our notation is more restrictive that ILFP, which was designed as a general logical notation to characterize the complete class of languages that are recognizable in polynomial time.
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.
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In each figure the upper graph shows the isolated constituent precision and the bottom graph shows the corresponding number of hypothesized constituents.
In this paper, Ben and Riloff present a coreference resolver called BABAR that focuses on the use of contextual-role knowledge for coreference resolution.
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We also performed experiments to evaluate the impact of each type of contextual role knowledge separately.
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.
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If all three sentences are in the same document, then even a human will find it difficult to classify McCann in (1) into either person or organization, unless there is some other information provided.
Due to many similarly performing systems, the author was not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics.
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We asked participants to each judge 200–300 sentences in terms of fluency and adequacy, the most commonly used manual evaluation metrics.
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM.
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To quantize, we use the binning method (Federico and Bertoldi, 2006) that sorts values, divides into equally sized bins, and averages within each bin.
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.
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The relativizer f(“that”) for example, may attach to an arbitrarily long relative clause that goes beyond token boundaries.
They have made use of local and global features to deal with the instances of same token in a document.
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The probability of the classes assigned to the words in a sentence in a document is defined as follows: where is determined by the maximum entropy classifier.
The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.
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Keyword detection error Even if a keyword consists of a single word, there are words which are not desirable as keywords for a domain.
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.
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By applying an inverse transformation to the output of the parser, arcs with non-standard labels can be lowered to their proper place in the dependency graph, giving rise 1The dependency graph has been modified to make the final period a dependent of the main verb instead of being a dependent of a special root node for the sentence. to non-projective structures.
Here both parametric and non-parametric models are explored.
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The resulting parsers surpass the best previously published performance results for the Penn Treebank.
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.
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Human judges also pointed out difficulties with the evaluation of long sentences.
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.
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We computed BLEU scores for each submission with a single reference translation.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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These are shown, with their associated costs, as follows: ABj nc 4.0 AB C/jj 6.0 CD /vb 5.
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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LCFRS's have only been loosely defined in this paper; we have yet to provide a complete set of formal properties associated with members of this class.
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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Each set is assigned two values: belief and plausibility.
They focused on phrases which two Named Entities, and proceed in two stages.
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Step 3.
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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3.
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.
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Given the bilingual graph described in the previous section, we can use label propagation to project the English POS labels to the foreign language.
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.
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A very small excerpt from an Italian-English graph is shown in Figure 1.
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 best answer to this is: many research labs have very competitive systems whose performance is hard to tell apart.
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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The 1st block contains the simple baselines from section 2.1.
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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Our suspicion is that BLEU is very sensitive to jargon, to selecting exactly the right words, and not synonyms that human judges may appreciate as equally good.
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.
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Consider the case where IX].
This paper talks about KenLM: Faster and Smaller Language Model Queries.
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Vocabulary lookup is a hash table mapping from word to vocabulary index.
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.
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It reads a file with a list of German connectives, and when a text is opened for annotation, it highlights all the words that show up in this list; these will be all the potential connectives.
Combining multiple highly-accurate independent parsers yields promising results.
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There are simply not enough votes remaining to allow any of the crossing structures to enter the hypothesized constituent set.
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.
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Reference resolution involves finding words that co-refer to the same entity.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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Now, for this application one might be tempted to simply bypass the segmentation problem and pronounce the text character-by-character.
This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers.
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It was then tested on section 22 of the Treebank in conjunction with the other parsers.
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.
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The dev and test sets were randomly chosen from the EMEA corpus.
The AdaBoost algorithm was developed for supervised learning.
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This section describes an algorithm based on boosting algorithms, which were previously developed for supervised machine learning problems.
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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(See also Wu and Fung [1994].)
In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures.
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As observed by Kahane et al. (1998), any (nonprojective) dependency graph can be transformed into a projective one by a lifting operation, which replaces each non-projective arc wj wk by a projective arc wi —* wk such that wi —*∗ wj holds in the original graph.
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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Table 1 shows four words “ 0 Indeed NN Indeed Saddamwhose unvocalized surface forms 0 an are indistinguishable.
This paper talks about KenLM: Faster and Smaller Language Model Queries.
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We substantially outperform all of them on query speed and offer lower memory consumption than lossless alternatives.
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.
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State will ultimately be used as context in a subsequent query.
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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2.
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.
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Almost all annotators reported difficulties in maintaining a consistent standard for fluency and adequacy judgements, but nevertheless most did not explicitly move towards a ranking-based evaluation.
This corpus has several advantages: it is annotated at different levels.
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Annotation of syntactic structure for the core corpus has just begun.
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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For example, both a chair and a truck would be labeled as artifacts, but this does not at all suggest that they are coreferent.
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.
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Figure 1 provides some statistics about this corpus.
Here we show how non-projective dependency parsing can be achieved by combining a data driven projective parser with special graph transformation techniques.
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In the first part of the experiment, dependency graphs from the treebanks were projectivized using the algorithm described in section 2.
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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As we have said, parse quality decreases with sentence length.
This paper talks about Unsupervised Models for Named Entity Classification.
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We also removed the context-type feature type when using the CoBoost approach.
This assumption, however, is not inherent to type-based tagging models.
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2 61.7 64.
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure.
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Since Daneˇs’ proposals of ‘thematic development patterns’, a few suggestions have been made as to the existence of a level of discourse structure that would predict the information structure of sentences within texts.
The AdaBoost algorithm was developed for supervised learning.
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For the moment we will assume that there are only two possible labels: each y, is in { —1, +1}.
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.
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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.
They have made use of local and global features to deal with the instances of same token in a document.
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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.
In order to handle the necessary word reordering as an optimization problem within the dynamic programming approach, they describe a solution to the traveling salesman problem (TSP) which is based on dynamic programming.
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Can we do . QmS: Yes, wonderful.
The second algorithm builds on a boosting algorithm called AdaBoost.
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The problem is a binary classification problem.
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.
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On the one hand, the type-level error rate is not calibrated for the number of n-grams in the sample.
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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As a partial solution, for pairs of hanzi that co-occur sufficiently often in our namelists, we use the estimated bigram cost, rather than the independence-based cost.
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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Among the IS-units, the referring expressions are marked as such and will in the second phase receive a label for cognitive status (active, accessible- text, accessible-situation, inferrable, inactive).
They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines.
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However, we note that the final conditional estimates p(s|t) from a given phrase table maximize the likelihood of joint empirical phrase pair counts over a word-aligned corpus.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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Since the transducers are built from human-readable descriptions using a lexical toolkit (Sproat 1995), the system is easily maintained and extended.
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems.
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Our assumption is that caseframes that co-occur in resolutions often have a 2 This normalization is performed syntactically without semantics, so the agent and patient roles are not guaranteed to hold, but they usually do in practice.
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.
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We also mark all tags that dominate a word with the feminine ending :: taa mar buuTa (markFeminine).
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.
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(2006) developed a technique for splitting and chunking long sentences.
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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The traveling salesman problem is an optimization problem which is defined as follows: given are a set of cities S = s1; ; sn and for each pair of cities si; sj the cost dij > 0 for traveling from city si to city sj . We are looking for the shortest tour visiting all cities exactly once while starting and ending in city s1.
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.
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The bootstrap method has been critized by Riezler and Maxwell (2005) and Collins et al. (2005), as being too optimistic in deciding for statistical significant difference between systems.
Human judges also pointed out difficulties with the evaluation of long sentences.
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In this shared task, we were also confronted with this problem, and since we had no funding for paying human judgements, we asked participants in the evaluation to share the burden.
It is probably the first analysis of Arabic parsing of this kind.
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The ATB annotation distinguishes between verbal and nominal readings of maSdar process nominals.
This paper presents methods to query N-gram language models, minimizing time and space costs.
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Sheffield Guthrie and Hepple (2010) explore several randomized compression techniques, but did not release code.
Combining multiple highly-accurate independent parsers yields promising results.
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In this case we are interested in finding' the maximum probability parse, ri, and Mi is the set of relevant (binary) parsing decisions made by parser i. ri is a parse selected from among the outputs of the individual parsers.
They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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Also there are cases where one of the two NEs belong to a phrase outside of the relation.
Human judges also pointed out difficulties with the evaluation of long sentences.
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The best answer to this is: many research labs have very competitive systems whose performance is hard to tell apart.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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This is an issue that we have not addressed at the current stage of our research.
The manual evaluation of scoring translation on a graded scale from 1–5 seems to be very hard to perform.
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Our initial experimentation with the evaluation tool showed that this is often too overwhelming.
They focused on phrases which two Named Entities, and proceed in two stages.
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The number of NE instance pairs used in their experiment is less than half of our method.
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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To check for this, we do pairwise bootstrap resampling: Again, we repeatedly sample sets of sentences, this time from both systems, and compare their BLEU scores on these sets.
They focused on phrases which two Named Entities, and proceed in two stages.
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As was explained in the results section, “strength” or “add” are not desirable keywords in the CC-domain.
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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In the numerator, however, the counts of ni1s are quite irregular, in­ cluding several zeros (e.g., RAT, none of whose members were seen).
This paper conducted research in the area of automatic paraphrase discovery.
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Overview of the method 2.2 Step by Step Algorithm.
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.
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Surprisingly, this effect is much less obvious for out-of-domain test data.
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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from the subset of the United Informatics corpus not used in the training of the models.
Here we present two algorithms.
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Roughly speaking, the new algorithm presented in this paper performs a similar search, but instead minimizes a bound on the number of (unlabeled) examples on which two classifiers disagree.