hrithikpiyush/acl-arc huggingface dataset:
6.1.1 Experiment 1 . Until now , translation models have been evaluated either subjectively ( e.g. White and O'Connell 1993 ) or using relative metrics , such as perplexity with respect to other models ( Brown et al. 1993b ) . Objective and more accurate tests can be carried out using a `` gold standard . ''
Due to the special structure of presentations , this corpus will be particularly demanding with respect to the required preprocessing components of an information retrieval system . The three preprocessing steps ( tokenization , POS-tagging , lemmatization ) are performed using TreeTagger ( Schmid , 1995 ) . The resulting list of POS-tagged lemmas is weighted using the SMART ` ltc ' 8 tf.idf-weighting scheme ( Salton , 1989 ) .
This means that the `` it '' that brought the disease in P1 will not be considered to refer to the infection `` i '' or the death `` d '' in P3 . This strategy is certainly the right one to start out with , since anaphora is always the more typical direction of reference in English prose ( Halliday and Hasan 1976 , p. 329 ) . Since techniques developed elsewhere may prove useful , at least for comparison , it is worth mentioning at this point that the proposed metarules are distant cousins of `` unique-name assumption '' ( Genesereth and Nilsson 1987 ) , `` domain closure assumption '' ( ibid . )
2 ) Parse trees are actually only used to model and explain the monolingual structure , rather than the bilingual mapping between language pairs . This indicates that parse trees are usually not the optimal choice for training tree-based translation models ( Wang et al. , 2010 ) . Based on the above analysis , we can conclude that the tree structure that is independent from Tree-bank resources and simultaneously considers the bilingual mapping inside the bilingual sentence pairs would be a good choice for building treebased translation models .
In addition , subcategorization requirements may vary across linguistic domain or genre ( Carroll and Rooth 1998 ) . Manning ( 1993 ) argues that , aside from missing domain-specific complementation trends , dictionaries produced by hand will tend to lag behind real language use because of their static nature . Given these facts , research on automating acquisition of dictionaries for lexically based NLP systems is a particularly important issue .
This library has been very popular because it allows researchers to experiment with different methods without having to modify code or reformat data . Finally , the Natural Language Toolkit ( NLTK ) is a package of NLP components implemented in Python ( Loper and Bird , 2002 ) . Python scripting is extremely simple to learn , read and write , and so using the existing components and designing new components is simple .
Developing a calculus for reasoning with QLFs is too large a task to be undertaken here . But the general outlines are reasonably clear , and we can adapt some of the UDRS ( Reyle 1995 ) work to our own framework . Reyle points out that many of the inferences involving underspecified representations that we would like to capture rely on the assumption that whatever context disambiguates the premise also disambiguates the conclusion , even if we do not know what that context or disambiguation is .
Hence MaltParser features are defined as POS tag at stk [ 0 ] , word-form at buf [ 0 ] , and so on . KUbler , McDonald , and Nivre ( 2009 ) describe a `` typical '' MaltParser model configuration of attributes and features .13 Starting with it , in a series of initial controlled experiments , we settled on using buf [ 0-1 ] + stk [ 0-1 ] for word-forms , and buf [ 0-3 ] + stk [ 0-2 ] for POS tags . For features of new MaltParser-attributes ( discussed later ) , we used buf [ 0 ] + stk [ 0 ] .
For some abstracts , MeSH headings can be used as ground truth , because one of the human indexers ' tasks in assigning terms is to identify the main topic of the article ( sometimes a disorder ) . For this evaluation , we randomly selected 50 abstracts with disorders indexed as the main topic from abstracts retrieved using PubMed on the five clinical questions described in Sneiderman et al. ( 2005 ) . We applied our problem extractor on different segments of the abstract : the title only , the title and first two sentences , and the entire abstract .
We can also hope for some fine-tuning of the notion of topic , which would prevent many offensive examples . This approach is taken in computational syntactic grammars ( e.g. Jensen 1986 ) ; the number of unlikely parses is severely reduced whenever possible , but no attempt is made to define only the so-called grammatical strings of a language . Finally , as the paragraph is a natural domain in which word senses can be reliably assigned to words or sentences can be syntactically disambiguated , larger chunks of discourse may be needed for precise assignment of topics , which we view as another type of disambiguation .
In addition , computationally treating lexical rules on a par with phrase structure rules fails to take computational advantage of their specific properties . For example , the interaction of lexical rules is explored at run-time , even though the possible interaction can be determined at compile-time given the information available in the lexical rules and the base lexical entries .2 Based on the research results reported in Meurers and Minnen ( 1995 , 1996 ) , we propose a new computational treatment of lexical rules that overcomes these shortcomings and results in a more efficient processing of lexical rules as used in HPSG . We developed a compiler that takes as its input a set of lexical rules , deduces the necessary transfer of properties not changed by the individual lexical rules , and encodes the set of lexical rules and their interaction into definite relations constraining lexical entries .
As might be expected , our evaluation measures disagree somewhat , but the following broad tendancies seem clear . On small data sets all of the Bayesian estimators strongly outperform EM ( and , to a lesser extent , VB ) with respect to all of our evaluation measures , confirming the results reported in Goldwater and Griffiths ( 2007 ) . This is perhaps not too surprising , as the Bayesian prior plays a comparatively stronger role with a smaller training corpus ( which makes the likelihood term smaller ) and the approximation used by Variational Bayes is likely to be less accurate on smaller data sets .
10 Some modification of this scheme is necessary when the input stream is not deterministic . For the A * algorithm ( Hart et al. 1968 ) as applied to speech recognition , the actual path score is typically augmented with an estimated score for the unseen portion . Unless some kind of normalization is done , the short theories have an unfair advantage , simply because fewer probability scores have been multiplied .
Figure 4 shows the architecture of Atlas-Andes ; any other system built with APE would look similar . Robust natural language understanding in Atlas-Andes is provided by Rosé 's CARMEL system ( Rosé 2000 ) ; it uses the spelling correction algorithm devised by Elmi and Evens ( 1998 ) .
8 History of the Framework and Comparison with Other Systems The framework represents a generalization of several predecessor NLG systems based on Meaning-Text Theory : FoG ( Kittredge and Polguere , 1991 ) , LFS ( Iordanskaja et al. , 1992 ) , and JOYCE ( Rambow and Korelsky , 1992 ) . The framework was originally developed for the realization of deep-syntactic structures in NLG ( Lavoie and Rambow , 1997 ) .
However , marker-normal form grammars can not capture the sorts of regularities demonstrated for languages that do not have a oneto-one mapping between a terminal symbol and a word . Nevertheless , Juola ( 1998 , page 23 ) observes that `` a slightly more general mapping , where two adjacent terminal symbols can be merged into a single lexical item ( for example , a word and its case-marking ) , can capture this sort of result quite handily . '' Work using the marker hypothesis for MT adapts this monolingual mapping for pairs of languages : It is reasonably straightforward to map an English determiner-noun sequence onto a Japanese noun -- case marker segment , once one has identified the sets of marker tags in the languages to be translated .
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 . Compared to their work , we do not rely on any Tree-bank resources and focus on generating effective unsupervised tree structures for tree-based translation models .
Our learning framework leaves open a number of design choices : 1 . Baseline language model : For P0 we used a trigram with modified kneser-ney smoothing [ Chen and Goodman , 1998 ] , which is still considered one of the best smoothing methods for n-gram language models . 2 .
The ACE-2004 dataset is annotated with both mention and mention heads , while the OntoNotes5 .0 dataset only has mention annotations . Therefore , we preprocess Ontonote-5 .0 to derive mention heads using Collins head rules ( Collins , 1999 ) with gold constituency parsing information and gold named entity information . The parsing information9 is only needed to generate training data for the mention head candidate generator and named entities are directly set as heads .
Second , the diversity of the individual grammars controls the gains that can be obtained by combining multiple grammars into a product model . Our most accurate single grammar achieves an F score of 91.6 on the WSJ test set , rivaling discriminative reranking approaches ( Charniak and Johnson , 2005 ) and products of latent variable grammars ( Petrov , 2010 ) , despite being a single generative PCFG . Our most accurate product model achieves an F score of 92.5 without the use of discriminative reranking and comes close to the best known numbers on this test set ( Zhang et al. , 2009 ) .
One of our long-term objectives is to identify a core set of features that will be useful for disambiguating a wide class of words using both supervised and unsupervised methodologies . We have presented an ensemble approach to word sense disambiguation ( Pedersen , 2000 ) where multiple Naive Bayesian classifiers , each based on co -- occurrence features from varying sized windows of context , is shown to perform well on the widely studied nouns interest and line . While the accuracy of this approach was as good as any previously published results , the learned models were complex and difficult to interpret , in effect acting as very accurate black boxes .
Rather , we stress the possibility that one can axiomatize and productively use such a rule . We shall see this in the next example : two sentences , regarded as a fragment of paragraph , are a variation on a theme by Hobbs ( 1979 ) .
This aspect of the system is beyond the scope of this paper , and therefore it will not be covered in detail . The recognizer for these systems is the SUMMIT system ( Zue et al. 1989 ) , which uses a segmental-based framework and includes an auditory model in the front-end processing . The lexicon is entered as phonetic pronunciations that are then augmented to account for a number of phonological rules .
Our acceptance step is therefore based on the remaining parameters : the context ( θLCTX , θRCTX ) . To sample from our proposal distribution , we use a blocked Gibbs sampler based on the one proposed by Goodman ( 1998 ) and used by Johnson et al. ( 2007 ) that samples entire parse trees . For a sentence w , the strategy is to use the Inside algorithm ( Lari and Young , 1990 ) to inductively compute , for each potential non-terminal position spanning words wi through wj − 1 and category t , going `` up '' the tree , the probability of generating wi , ... , wj − 1 via any arrangement of productions that is rooted by yij = t.
Thus the semantic representations we assume are simply set of literals of the form Pn ( x1 , ... , xn ) where Pn is a predicate of arity n and xi is either a constant or a unification variable whose value will be instantiated during processing . Semantic construction proceeds from the derived tree ( Gardent and Kallmeyer , 2003 ) rather than -- as is more common in TAG -- from the derivation tree . This is done by associating each elementary tree with a semantic representation and by decorating relevant tree nodes with unification variables and constants occuring in associated semantic representation .
Our baseline coreference system uses the C4 .5 decision tree learner ( Quinlan , 1993 ) to acquire a classifier on the training texts for determining whether two NPs are coreferent . Following previous work ( e.g. , Soon et al. ( 2001 ) and Ponzetto and Strube ( 2006 ) ) , we generate training instances as follows : a positive instance is created for each anaphoric NP , NPj , and its closest antecedent , NPi ; and a negative instance is created for NPj paired with each of the intervening NPs , NPi +1 , NPi +2 , ... , NPj_1 . Each instance is represented by 33 lexical , grammatical , semantic , and
In this paper , we use TF-IDF ( a kind of augmented DF ) as a feature selection criterion , in order to ensure results are comparable with those in ( Yahyaoui , 2001 ) . TF-IDF ( term frequency-inverse document frequency ) is one of the widely used feature selection techniques in information retrieval ( Yates and Neto , 1999 ) . Specifically , it is used as a metric for measuring the importance of a word in a document within a collection , so as to improve the recall and the precision of the retrieved documents .
( Chomsky 1977 ) . The gap mechanism resembles the Hold register idea of ATNs ( Woods 1970 ) and the treatment of bounded domination metavariables in lexical functional grammars ( LFGs ) ( Bresnan 1982 , p. 235 ff . ) , but it is different from these in that the process of filling the Hold register equivalent involves two steps separately initiated by two independent nodes .
Prior work using mLDA has used Gibbs Sampling to approximate the posterior , but we found this method did not scale with larger values of K , especially when applied to the relatively large deWaC corpus . To solve these scaling issues , we implement Online Variational Bayesian Inference ( Hoffman et al. , 2010 ; Hoffman et al. , 2012 ) for our models . In Variational Bayesian Inference ( VBI ) , one approximates the true posterior using simpler distributions with free variables .