hrithikpiyush/acl-arc huggingface dataset:
If a new input string can not be found exactly in the translation database , a search is conducted for close ( or `` fuzzy '' ) matches of the input string , and these are retrieved together with their translations for the translator to manipulate into the final , output translation . From this description , it should be clear that TM systems do not translate : Indeed , some researchers consider them to be little more than a search-and-replace engine , albeit a rather sophisticated one ( Macklovitch and Russell 2000 ) . We can illustrate this with respect to the TM entries in ( 1 ) , taken from the Canadian Hansards : ( 1 ) a .
Following this , we split the predicate instances evenly into ten folds and used each fold as testing data for a model trained on the instances outside the fold . This evaluation set-up is an improvement versus the one we previously reported ( Gerber and Chai 2010 ) , in which fixed partitions were used for training , development , and testing . During training , the system was provided with annotated predicate instances .
For instance , Blume ( 2005 ) uses NEs coocurring with the ambiguous mentions of a name as a key feature for the disambiguation process . Saggion ( 2008 ) compared the performace of NEs versus BoW features . In his experiments a only a representation based on Organisation NEs outperformed the word based approach .
In order to encourage our model to choose trees in which the constituent labels `` fit '' into their supertag contexts , we want to bias our context parameters toward context categories that are combinable with the constituent label . The right-side context of a non-terminal category -- the probability of generating a category to the right of the current constituent 's category -- corresponds directly to the category transitions used for the HMM supertagger of Garrette et al. ( 2014 ) . Thus , the right-side context prior mean θRCTX-0 can be biased in exactly the same way as t the HMM supertagger 's transitions : toward context supertags that connect to the constituent label .
5.1 Core Language Engine Quasi-Logical Form The starting point for the approach followed here was a dissatisfaction with certain aspects of the theory of quasi-logical form as described in Alshawi ( 1990 , 1992 ) , and implemented in SRI 's Core Language Engine ( CLE ) . In the CLE-QLF approach , as ra -
The UniProt Non-redundant Reference ( UniRef ) databases combine closely related sequences into a single record where similar sequences are grouped together . Three UniRef tables UniRef100 , UniRef90 and UniRef50 ) are available for download : UniRef100 combines identical sequences and sub-fragments into a single UniRef entry ; and UniRef90 and UniRef50 are built by clustering UniRef100 sequences into clusters based on the CD-HIT algorithm [ 16 ] such that each cluster is composed of sequences that have at least 90 % or 50 % sequence similarity , respectively , to the representative sequence . NCBI resources -- three data sources from NCBI were used in this study : GenPept , RefSeq , and Entrez GENE .
The Computer Vision community has also benefited greatly from efforts to unify the two modalities . To name a few examples , Rohrbach et al. ( 2010 ) and Socher et al. ( 2013 ) show how semantic information from text can be used to improve zero-shot classification ( i.e. , classifying never-before-seen objects ) , and Motwani and Mooney ( 2012 ) show that verb clusters can be used to improve activity recognition in videos .
The computational treatment of lexical rules as covariation in lexical entries was implemented in Prolog by the authors in cooperation with Dieter Martini for the ConTroll system ( Gerdemann and King 1994 ; Gotz and Meurers 1997a ) . We tested the covariation approach with a complex grammar implementing an HPSG analysis covering the so-called aux-flip phenomenon , and partial-VP topicalization in the three clause types of German ( Hinrichs , Meurers , and Nakazawa 1994 ) .
Also , the prepositions ( i.e. , and ) not be considered a part of the mention . Arabic has two kinds of plurals : broken plurals and sound plurals ( Wightwick and Gaafar , 1998 ; Chen and Gey , 2002 ) . The formation of broken plurals is common , more complex and often irregular .
For example , when using BLEU , it makes a big difference whether individual sentences are both longer and shorter than the reference or systematically shorter than the reference , but these two cases can not be distinguished by single-sentence objective functions . Our plan is to implement a windowed or moving-average version of BLEU as in ( Chiang et al. , 2008 ) . We also plan to further speed up the tuning process by parallelizing the decoding of the sentences in the tuning set .
Disambiguation of capitalized words is usually handled by POS taggers , which treat capitalized words in the same way as other categories , that is , by accounting for the immediate syntactic context and using estimates collected from a training corpus . As Church ( 1988 ) rightly pointed out , however , `` Proper nouns and capitalized words are particularly problematic : some capitalized words are proper nouns and some are not . Estimates from the Brown Corpus can be misleading .
Many ACE participants have tic classes . For instance , part of the ACE Phase 2 also adopted a corpus-based approach to SC deterevaluation involves classifying an NP as PERSON , mination that is investigated as part of the mention ORGANIZATION , GPE ( a geographical-political redetection ( MD ) task ( e.g. , Florian et al. ( 2006 ) ) . gion ) , FACILITY , LOCATION , or OTHERS .
This includes work on question answering ( Wang et al. , 2007 ) , sentiment analysis ( Nakagawa et al. , 2010 ) , MT reordering ( Xu et al. , 2009 ) , and many other tasks . In most cases , the accuracy of parsers degrades when run on out-of-domain data ( Gildea , 2001 ; McClosky et al. , 2006 ; Blitzer et al. , 2006 ; Petrov et al. , 2010 ) . But these accuracies are measured with respect to gold-standard out-of-domain parse trees .
That is , for each mi , j , we have a corresponding head ha , b where i < a < b < j. Moreover , for another head ha ' , b ' , we have the satisfying condition a − b ' > 0 or b − a ' < 0 bha , b , ha ' , b ' . Based on this assumption , the problem of identifying mention heads is a sequential phrase identification problem , and we choose to employ the BILOU-representation as it has advantages over traditional BIO-representation , as shown , e.g. in Ratinov and Roth ( 2009 ) . The BILOUrepresentation suggests learning classifiers that identify the Beginning , Inside and Last tokens of multi-token chunks as well as Unit-length chunks .
The importance of each term is assumed to be inversely proportional to the number of documents that contain that term . TF is given by TFD , t , and it denotes frequency of term t in document D. IDF is given by IDFt = log ( N/dft ) , where N is the number of documents in the collection , and dft is the number of documents containing the term t. ( Salton and Yang , 1973 ) proposed the combination of TF and IDF as weighting schemes , and it has been shown that their product gave better performance . Thus , the weight of each term/root in a document is given by wD , t = TFD , t * IDFt .
An HPSG grammar consists of lexical entries and ID grammar rules , each of which is described with typed feature structures ( Carpenter , 1992 ) . A lexical entry for each word expresses the characteristics of the word , such as the subcategorization frame and the grammatical category .
There is , however , a risk of overfitting when evaluation is limited to a gold standard of this size . More recently , Burke , Cahill , et al. ( 2004a ) carried out an evaluation of the automatic annotation algorithm against the publicly available PARC 700 Dependency Bank ( King et al. 2003 ) , a set of 700 randomly selected sentences from Section 23 which have been parsed , converted to dependency format , and manually corrected and extended by human validators . They report precision of over 88.5 % and recall of over 86 % ( Table 2 ) .
measure the standard intrinsic parser metrics unlabeled attachment score ( UAS ) and labeled attachment score ( LAS ) ( Buchholz and Marsi , 2006 ) . In terms of treebank data , the primary training corpus is the Penn Wall Street Journal Treebank ( PTB ) ( Marcus et al. , 1993 ) . We also make use of the Brown corpus , and the Question Treebank ( QTB ) ( Judge et al. , 2006 ) .
For these reasons , explicit section markers are not entirely reliable indicators for the various semantic elements we seek to extract , but must be considered along with other sources of evidence . The extraction of each PICO element relies to a different extent on an annotated corpus of MEDLINE abstracts , created through an effort led by the first author at the National Library of Medicine ( Demner-Fushman et al. 2006 ) . As will be described herein , the population , problem , and the intervention extractors are based largely on recognition of semantic types and a few manually constructed rules ; the outcome extrac -
For example , we might have a class Mexican Food and learn that the entire class is suitable for eating . Usually , the classes are from WordNet ( Miller et al. , 1990 ) , although they can also be inferred from clustering ( Rooth et al. , 1999 ) . Brockmann and Lapata ( 2003 ) compare a number of WordNet-based approaches , including Resnik ( 1996 ) , Li and Abe ( 1998 ) , and Clark and Weir ( 2002 ) , and found that the more sophisticated class-based approaches do not always outperform simple frequency-based models .
Incrementality : Help or Hindrance ? The account sketched in Section 4 was superimposed on an incremental GRE algorithm , partly because incrementality is well established in this area ( Appelt 1985 ; Dale and Reiter 1995 ) . But IA may be replaced by any other reasonable7 GRE algorithm , for example , one that always exactly minimizes the number of properties expressed , or one that always `` greedily '' selects the property that removes the maximum number of distractors .
Now for some important remarks on efficiency : • Computing ti is an instance of the well-known algebraic path problem ( Lehmann , 1977 ; Tar an , 1981a ) . Let Ti = xiofoyi .
In a similar vein , Steyvers ( 2010 ) showed that a different feature-topic model improved predictions on a fill-in-the-blank task . Johns and Jones ( 2012 ) take an entirely different approach by showing that one can successfully infer held out feature norms from weighted mixtures based on textual similarity . Silberer and Lapata ( 2012 ) introduce a new method of multimodal integration based on Canonical Correlation Analysis , and performs a systematic comparison between their CCA-based model and others on association norm prediction , held out feature prediction , and word similarity .
Previous work in sentence planning in the natural language generation ( NLG ) community uses hand-written rules to approximate the distribution of linguistic phenomena in a corpus ( see ( Shaw , 1998 ) for a recent example with further references ) . This approach is difficult to scale due to the nonrobustness of rules and unexpected interactions ( Hovy and Wanner , 1996 ) , and it is difficult to develop new applications quickly .
ex istsl ( e. pos ( pres ( u se ( e , eve ry ( m a na ger ) , a c. , „„ ( com put er ) ) ) ) We assume that every determiner has its own equivalence , which resolves it as a quantifier : sometimes this can be quite a complicated matter , as with any ( Alshawi 1990 ) , which will resolve in different ways depending on its linguistic context , but here we avoid this complexity ' 6 Separate equivalences might also make it easier to encode determiner-specific preferences , such as that of each for wide scope . A referee points out that the lack of any explicit ordering of application of equivalences makes one natural way of doing this unavailable .
This article focuses on gradable adjectives , also called degree adjectives .1 More specifically , we shall explore how referring expressions containing gradable adjectives can be produced by a Natural Language Generation ( NLG ) program . Following Pinkal ( 1979 ) , such expressions will be called vague descriptions even though , as we shall see , the vagueness of the adjective does not extend to the description as a whole . It will be useful to generalize over different forms of the adjective , covering the superlative form ( e.g. , largest ) and the comparative form ( larger ) , as well as the positive or base form ( large ) of the adjective .
Since it is not sensible to evaluate association norm prediction when they are also used as training data , we omit this evaluation for this modality . Following Andrews et al. ( 2009 ) , we measure association norm prediction as an average of percentile ranks . For all possible pairs of words in our vocabulary , we compute the negative symmetric KL divergence between the two words .
Cohn and Blunsom ( 2009 ) adopted a Bayesian method to infer an STSG by exploring the space of alignments based on parse trees . Liu et al. ( 2012 ) re-trained the linguistic parsers bilingually based on word alignment . Burkett and Klein ( 2012 ) utilized a transformation-based method to learn a sequence of monolingual tree transformations for translation .
Much of theoretical linguistics can be formulated in a very natural manner as stating correspondences ( translations ) between layers of representation structures ( Rambow & Satta , 1996 ) , such as the relation between syntax and semantic . An analogous problem is to be defined in such a way that expresses the correspondence between a language and its translations in other languages .
This means that the linguistic realization can not start until CD is concluded , contradicting eye-tracking experiments suggesting that speakers start speaking while still scanning distractors ( Pechmann 1989 ) . A similar problem is discussed in the psycholinguistics of interpretation ( Sedivy et al. 1999 ) : Interpretation is widely assumed to proceed incrementally , but vague descriptions resist strict incrementality , since an adjective in a vague description can only be fully interpreted when its comparison set is known . Sedivy and colleagues resolve this quandary by allowing a kind of revision , whereby later words allow hearers to refine their interpretation of gradable adjectives .
In this paper , we evaluated the role of low-level image features , SURF and GIST , for their compatibility with the multimodal Latent Dirichlet Allocation model of Andrews et al. ( 2009 ) . We found both fea -
In the implemented applications , the DSyntSs are the pivotal representations involved in most transformations , as this is also often the case in practice in linguistic-based MT ( Hutchins and Somers , 1997 ) . Figure 2 illustrates a DSyntS from a meteorological application , MeteoCogent ( Kittredge and Lavoie , 1998 ) , represented using the standard graphical notation and also the RealPro ASCII notation used internally in the framework ( Lavoie and Rambow , 1997 ) . As Figure 2 illustrates , there is a straightforward mapping between the graphical notation and the ASCII notation supported in the framework .
Somers ( 1998 ) replicates the work of Fung and McKeown with different language pairs using the simpler metric of Levenshtein distance . Boutsis and Piperidis ( 1998 ) use a tagged parallel corpus to extract translationally equivalent English-Greek clauses on the basis of word occurrence and co-occurrence probabilities . The respective lengths of the putative alignments in terms of characters is also an important factor .
to model compounds , a highly productive phenomenon in German ( see Section 8 ) . The key linguistic knowledge sources that we use are morphological analysis and generation of German based on SMOR , a morphological analyzer/generator of German ( Schmid et al. , 2004 ) and the BitPar parser , which is a state-of-the-art parser of German ( Schmid , 2004 ) .
A common computational treatment of lexical rules adopted , for example , in the ALE system ( Carpenter and Penn 1994 ) consists of computing the transitive closure of the base lexical entries under lexical rule application at compile-time . While this provides a front-end to include lexical rules in the grammars , it has the disadvantage that the generalizations captured by lexical rules are not used for computation .
Miyao , Ninomiya , and Tsujii ( 2004 ) and Nakanishi , Miyao , and Tsujii ( 2004 ) describe a methodology for acquiring an English HPSG from the Penn-II Treebank . Manually defined heuristics are used to automatically annotate each tree in the treebank with partially specified HPSG derivation trees : Head/argument/modifier distinctions are made for each node in the tree based on Magerman ( 1994 ) and Collins ( 1997 ) ; the whole tree is then converted to a binary tree ; heuristics are applied to deal with phenomena such as LDDs and coordination and to correct some errors in the treebank , and finally an HPSG category is assigned to each node in the tree in accordance with its CFG category .
The lexia will be marked as a derivative work from the original one , and a new document history timeline will start ( see Figure 2 ) . Authors may choose this right with the No-Deriv option of the Creative Commons licences ( Lessig , 2004 ) . If nobody claims the document for himself , it will fall in the public domain .
According to Budanitsky and Hirst ( 2006 ) , there are three prevalent approaches for evaluating SR measures : mathematical analysis , applicationspecific evaluation and comparison with human judgments . Mathematical analysis can assess a measure with respect to some formal properties , e.g. whether a measure is a metric ( Lin , 1998 ) .4 However , mathematical analysis can not tell us whether a measure closely resembles human judgments or whether it performs best when used in a certain application . The latter question is tackled by applicationspecific evaluation , where a measure is tested within the framework of a certain application , e.g. word sense disambiguation ( Patwardhan et al. , 2003 ) or malapropism detection ( Budanitsky and Hirst , 2006 ) .
One of garbage , non-angry , slightly angry , very angry . The same annotation scheme as in our previous work on anger detection has been applied , see e.g. ( Schmitt et al. , 2009 ) . From all 4,832 user turns , 68.5 % were non-angry , 14.3 % slightly angry , 5.0 % very angry and 12.2 % contained garbage , i.e. nonspeech events .
3http : / / simplecv.org We also compute GIST vectors ( Oliva and Torralba , 2001 ) for every image using LearGIST ( Douze et al. , 2009 ) . Unlike SURF descriptors , GIST produces a single vector representation for an image .
In dialogue management and generation , the key issue we are planning to investigate is that of linguistic alignment . The analysis of the data we have collected indicates that student satisfaction may be affected if the system rephrases student answers using different words ( for example , using better terminology ) but does n't explicitly explain the reason why different terminology is needed ( Dzikovska et al. , 2010 ) . Results from other systems show that measures of semantic coherence between a student and a system were positively associated with higher learning gain ( Ward and Litman , 2006 ) .
A signature is interpreted as follows : Every object is assigned exactly one most specific type , and in case a feature is appropriate for some object of a certain type , then it is appropriate for all objects of this type . ' A logic that provides the formal architecture required by Pollard and Sag ( 1994 ) was defined by King ( 1989 , 1994 ) . The formal language of King allows the expression of grammatical principles using type assignments to refer to the type of an object and path equalities to require the ( token ) identity of objects .
As an example , scientific abstracts across many different fields generally follow the pattern of `` introduction '' , `` methods '' , `` results '' , and `` conclusions '' ( SalangerMeyer , 1990 ; Swales , 1990 ; Or˘asan , 2001 ) . The ability to explicitly identify these sections in unstructured text could play an important role in applications such as document summarization ( Teufel and Moens , 2000 ) , information retrieval ( Tbahriti et al. , 2005 ) , information extraction ( Mizuta et al. , 2005 ) , and question answering .
Much work has been done on the use of morphological features for parsing of morphologically rich languages . Collins et al. ( 1999 ) report that an optimal tag set for parsing Czech consists of a basic POS tag plus a CASE feature ( when applicable ) . This tag set ( size 58 ) outperforms the basic Czech POS tag set ( size 13 ) and the complete tag set ( size ≈ 3000 + ) .
In all our experiments , we contrasted the results obtained using machine-predicted input with the results obtained using gold input ( the 9 We do not relate to specific results in their study because it has been brought to our attention that Hohensee and Bender ( 2012 ) are in the process of rechecking their code for errors , and rerunning their experiments ( personal communication ) . upper bound for using these features ) .
The second is more complicated . Inspired by ( Klein and Manning , 2003 ) , we split one phrase type into several subsymbols , which contain category information of current constituent 's parent . For example , an NP immediately dominated by a S , will be substituted by NPˆS .
F-structures are attribute -- value structures which represent abstract syntactic information , approximating to basic predicate -- argument -- modifier structures . Most of the early work on automatic f-structure annotation ( e.g. , van Genabith , Way , and Sadler 1999 ; Frank 2000 ; Sadler , van Genabith , and Way 2000 ) was applied only to small data sets ( fewer than 200 sentences ) and was largely proof of concept . However , more recent work ( Cahill et al. 2002 ; Cahill , McCarthy , et al. 2004 ) has presented efforts in evolving and scaling up annotation techniques to the Penn-II Treebank ( Marcus et al. 1994 ) , containing more than 1,000,000 words and 49,000 sentences .
We prepare the training data by splitting compounds in two steps , following the technique of Fritzinger and Fraser ( 2010 ) . First , possible split points are extracted using SMOR , and second , the best split points are selected using the geometric mean of word part frequencies .
Briscoe and Carroll ( 1997 ) refine the BHT with a priori information about the probabilities of subcategorization frame membership and use it to filter the induced frames . Recent work by Korhonen ( 2002 ) on the filtering phase of this approach uses linguistic verb classes ( based on Levin [ 1993 ] ) for obtaining more accurate back-off estimates for hypothesis selection . Carroll and Rooth ( 1998 ) use a handwritten head-lexicalized , context-free grammar and a text corpus to compute the probability of particular subcategorization patterns .
We set the MI-threshold , T , to be 0 , and the negative-to-positive ratio , K , to be 2 . Numerous previous pseudodisambiguation evaluations only include arguments that occur between 30 and 3000 times ( Erk , 2007 ; Keller and Lapata , 2003 ; Rooth et al. , 1999 ) . Presumably the lower bound is to help ensure the negative argument is unobserved because it is unsuitable , not because of data sparseness .
In contrast , the bottom feature structure encodes information that remains local to the node at which adjunction takes place . The language chosen for semantic representation is a flat semantics along the line of ( Bos , 1995 ; Copestake et al. , 1999 ; Copestake et al. , 2001 ) . However because we are here focusing on paraphrases rather than fine grained semantic distinctions , the underspecification and the description of the scope relations permitted by these semantics will here be largely ignored and flat semantics will be principally used as a convenient way of describing predicate/arguments and modifiers/modified relationships .