hrithikpiyush/acl-arc huggingface dataset:
Others have looked at the application of machine learning algorithms to annotated multimodal corpora . For example , Jokinen and Ragni ( 2007 ) and Jokinen et al. ( 2008 ) find that machine learning algorithms can be trained to recognise some of the functions of head movements , while Reidsma et al. ( 2009 ) show that there is a dependence between focus of attention and assignment of dialogue act labels . Related are also the studies by Rieks op den Akker and Schulz ( 2008 ) and Murray and Renals ( 2008 ) : both achieve promising results in the automatic segmentation of dialogue acts using the annotations in a large multimodal corpus .
This Principle of Finitism is also assumed by Johnson-Laird ( 1983 ) , Jackendoff ( 1983 ) , Kamp ( 1981 ) , and implicitly or explicitly by almost all researchers in computational linguistics . As a logical postulate it is not very radical ; it is possible within a finitary framework to develop that part of mathematics that is used or has potential applications in natural science , such as mathematical analysis ( cfXXX Mycielski 1981 ) . On the other hand , a possible obstacle to our strategy of using only finite objects is the fact that the deductive closure of any set of formulas is not finite in standard logic , while , clearly , we will have to deduce new facts from formal representations of text and background knowledge .
An interesting aspect of our generative approach is that we model HMM outputs as Gaussian vectors ( log probabilities of observing entire sentences based on our language models ) , as opposed to sequences of terms , as done in ( Barzilay and Lee , 2004 ) . This technique provides two important advantages .
The marker hypothesis has been used for a number of different language-related tasks , including • language learning ( Green 1979 ; Mori and Moeser 1983 ; Morgan , Meier , and Newport 1989 ) • monolingual grammar induction ( Juola 1998 ) • grammar optimization ( Juola 1994 ) • insights into universal grammar ( Juola 1998 ) • machine translation ( Juola 1994 , 1997 ; Veale and Way 1997 ; Gough , Way , and Hearne 2002 ) With respect to translation , a potential problem in using the marker hypothesis is that some languages do not have marker words such as articles , for instance .
The rate of accession may also be represented graphically . In Charniak ( 1996 ) and Krotov et al. ( 1998 ) , it was observed that treebank grammars ( CFGs extracted from treebanks ) are very large and grow with the size of the treebank . We were interested in discovering whether the acquisition of lexical material from the same data displayed a similar propensity .
In this paper , we show that the strongly equivalent grammars enable the sharing of `` parsing techniques '' , which are dependent on each computational framework and have never been shared among HPSG and LTAG communities . We apply our system to the latest version of the XTAG English grammar ( The XTAG Research Group , 2001 ) , which is a large-scale FB-LTAG grammar . A parsing experiment shows that an efficient HPSG parser with the obtained grammar achieved a significant speed-up against an existing LTAG parser ( Yoshinaga et al. , 2001 ) .
The confluence of these many factors makes clinical question answering a very exciting area of research . Furthermore , the need to answer questions related to patient care at the point of service has been well studied and documented ( Covell , Uman , and Manning 1985 ; Gorman , Ash , and Wykoff 1994 ; Ely et al. 1999 , 2005 ) . MEDLINE , the authoritative repository of abstracts from the medical and biomedical primary literature maintained by the National Library of Medicine , provides the clinically relevant sources for answering physicians ' questions , and is commonly used in that capacity ( Cogdill and Moore 1997 ; De Groote and Dorsch 2003 ) .
If we start by considering languages with a labeled attachment score of 85 % or higher , they are characterized by high precision and recall for root nodes , typically 95/90 , and by a graceful degradation of attachment score as arcs grow longer , typically 95 -- 90 -- 85 , for arcs of length 1 , 2 and 3 -- 6 . Typical examples are Bulgarian ( Simov et al. , 2005 ; Simov and Osenova , 2003 ) , Chinese ( Chen et al. , 2003 ) , Danish ( Kromann , 2003 ) , and Swedish ( Nilsson et al. , 2005 ) . Japanese ( Kawata and Bartels , 2000 ) , despite a very high accuracy , is different in that attachment score drops from 98 % to 85 % , as we go from length 1 to 2 , which may have something to do with the data consisting of transcribed speech with very short utterances .
In a vague description , the property last added to the description is context dependent . Worst case , calculating the set corresponding with such a property , of the form size ( x ) = maxm , for example , involves sorting the distractors as to their size , which may amount to O ( n2d ) or O ( nd log nd ) calculations ( depending on the sorting algorithm : cfXXX [ Aho et al. 1983 ] Chapter 8 ) . Once again , the most time-consuming part of the calculation can be performed off-line , since it is the same for all referring expressions .
As parsing performance improves , the performance of linguistic-feature-based approaches will increase . Virpioja et al. ( 2007 ) , Badr et al. ( 2008 ) , Luong et al. ( 2010 ) , Clifton and Sarkar ( 2011 ) , and others are primarily concerned with using morpheme segmentation in SMT , which is a useful approach for dealing with issues of word-formation . However , this does not deal directly with linguistic features marked by inflection .
and its degree of overgeneration ( does it generate only the sentences of the described language ? ) While corpus driven efforts along the PARSEVAL lines ( Black et al. , 1991 ) are good at giving some measure of a grammar coverage , they are not suitable for finer grained analysis and in particular , for progress evaluation , regression testing and comparative report generation . Another known method consists in developing and using a test suite that is , a set of negative and positive items against which the grammar can be systematically tested .
Since , unlike English , Russian is a highly inflected language , we had to deal with the case normalization issue . Before using the DCA method , we applied a Russian morphological processor ( Mikheev and Liubushkina 1995 ) to convert each word in the text to its main form : nominative case singular for nouns and adjectives , infinitive for verbs , etc. . For words that could be normalized to several main forms ( polysemy ) , when secondary forms of different words coincided , we retained all the main forms .
While such an approach promises much in terms of emerging infrastructure , we wish to exploit existing computing infrastructure that is more accessible to linguists via a P2P approach . In simple terms , P2P is a technology that takes advantage of the resources and services available at the edge of the Internet ( Shirky , 2001 ) . Better known for file-sharing and Instant Messenger applications , P2P has increasingly been applied in distributed computational systems .
In this section , we describe a meta-level process which can automatically select a response-generation method to address a new request without using such thresholds . A common way to combine different models consists of selecting the model that is most confident regarding its decision ( Burke 2002 ) . However , in our case , the individual confidence ( applicability ) measures employed by our response-generation methods are not comparable ( e.g. , the retrieval score in Doc-Ret is different in nature from the prediction probability in Doc-Pred ) .
Common sense ( as well as the Gricean maxims ; Grice 1975 ) suggests that vague descriptions are preferred by speakers over quantitative ones whenever the additional information provided by a quantitative description is irrelevant to the purpose of the communication . We are not aware of any empirical validation of this idea , but the fact that vague descriptions are frequent is fairly well documented .
For this we need to combine the highlevel rules with rules for paraphrasing the text in the monologue with text for the dialogue acts that express the same information in dialogue form . For automatically extracting these surface level mappings we will draw on the approach to learning paraphrases from a corpus that is described in Barzilay and McKeown ( 2001 ) . An important component of our future effort will be to evaluate whether automatically generating dialogues from naturally-occurring monologues , following the approach described here , results in dialogues that are fluent and coherent and preserve the information from the input monologue .
The formalization of DLRs provided by Meurers ( 1995 ) defines a formal lexical rule specification language and provides a semantics for that language in two steps : A rewrite system enriches the lexical rule specification into a fully explicit description of the kind shown in Figure 1 . This description can then be given the standard set-theoretical interpretation of King ( 1989 , 1994 ) . ' 10 Note that the passivization lexical rule in Figure 2 is only intended to illustrate the mechanism .
cue word and name the first ( or several ) associated words that come to mind ( e.g. , Nelson et al. ( 2004 ) ) , and feature norms , where subjects are given a cue word and asked to describe typical properties of the cue concept ( e.g. , McRae et al. ( 2005 ) ) . Griffiths et al. ( 2007 ) helped pave the path for cognitive-linguistic multimodal research , showing that Latent Dirichlet Allocation outperformed Latent Semantic Analysis ( Deerwester et al. , 1990 ) in the prediction of association norms . Andrews et al. ( 2009 ) furthered this work by showing that a bimodal topic model , consisting of both text and feature norms , outperformed models using only one modality on the prediction of association norms , word substitution errors , and semantic interference tasks .
FOG does not appear to have generic rules governing the use of gradable notions : it does not compute the meaning of a vague term based on the context , but uses fixed boundary values instead . A more flexible approach is used by Reiter and Sripada ( 2002 ) , where users can specify boundary values for attributes like rainfall , specifying , for example , rain counts as moderate above 7 mm/h , as heavy above 20 mm/h , and so on . A third approach was implemented in Dial Your Disc ( DYD ) , where the extension of a gradable adjective like famous was computed rather than specified by hand ( van Deemter and Odijk 1997 ) .
feature sets , but then efficiency becomes a problem . Collins and Duffy ( 2002 ) define a kernel over parse trees and apply it to re-ranking the output of a parser , but the resulting feature space is restricted by the need to compute the kernel efficiently , and the results are not as good as Collins ' previous work on re-ranking using a finite set of features ( Collins , 2000 ) . In this work we use a method for automatically inducing a finite set of features for representing the derivation history .
The reported results for the full parse tree ( on section 23 ) are recall/precision of 88.1 / 87.5 ( Collins , 1997 ) . The shallow parser used is the SNoW-based CSCL parser ( Punyakanok and Roth , 2001 ; Munoz et al. , 1999 ) . SNoW ( Carleson et al. , 1999 ; Roth , 1998 ) is a multi-class classifier that is specifically tailored for learning in domains in which the potential number of information sources ( features ) taking part in decisions is very large , of which NLP is a principal example .
Generalization for Online-to-Batch Conversion . In practice , perceptron-type algorithms are often applied in a batch learning scenario , i.e. , the algorithm is applied for K epochs to a training sample of size T and then used for prediction on an unseen test set ( Freund and Schapire , 1999 ; Collins , 2002 ) . The difference to the online learning scenario is that we treat the multi-epoch algorithm as an empirical risk minimizer that selects a final weight vector wT , K whose expected loss on unseen data we would like to bound .
We start by reporting the results in which we compare the full parser and the shallow parser on the `` clean '' WSJ data . Table 2 shows the results on identifying all phrases -- chunking in CoNLL2000 ( Tjong Kim Sang and Buchholz , 2000 ) terminology . The results show that for the tasks of identifying phrases , learning directly , as done by the shallow parser outperforms the outcome from the full parser .
As these are computationally very efficient , the authors could use a variant of Gibbs sampling for generating negative samples . Unfortunately , as shown in ( Okanohara and Tsujii , 2007 ) , with the represetation of sentences that we use , linear classifiers can not discriminate real sentences from sentences sampled from a trigram , which is the model we use as a baseline , so here we resort to a non-linear large-margin classifier ( see section 3 for details ) . While large-margin classifiers consistently out-perform other learning algorithms in many NLP tasks , their non-linear variations are also notoriously slow when it comes to computing their decision function -- taking time that can be linear in the size of their training data .
Perhaps a graphical rendering of the semantic predicates present in relevant abstracts might more effectively convey the desired information ; see , for example , Fiszman , Rindflesch , and Kilicoglu ( 2004 ) . Perhaps some variation of multi-level bulleted lists , appropriately integrated with interface elements for expanding and hiding items , might provide physicians a better overview of the information landscape ; see , for example , Demner-Fushman and Lin ( 2006 ) . Recognizing this complex set of issues , we decided to take a simple extractive approach to answer generation .
The work of Sarkar ( 2001 ) and Steedman , Osborne , et al. ( 2003 ) suggests that co-training can be helpful for statistical parsing . Pierce and Cardie ( 2001 ) have shown , in the context of base noun identification , that combining sample selection and cotraining can be an effective learning framework for large-scale training . Similar approaches are being explored for parsing ( Steedman , Hwa , et al. 2003 ; Hwa et al. 2003 ) .
Four algorithms are compared during the following experiments : • The regular TBL , as described in section 2 ; • An improved version of TBL , which makes extensive use of indexes to speed up the rules ' update ; • The FastTBL algorithm ; • The ICA algorithm ( Hepple , 2000 ) .
In the absence of a sufficient amount of training data , some arcs will inevitably be zeroed out . Unless it is desired to intentionally filter these out as being outside of the new domain , one can insert some arbitrarily small probability for these arcs , using , for example , an N-gram back-off model ( Katz 1987 ) .
The key for quality improvement seems to be rooted mainly in the presence or absence of some form of dictionary . Empirical evidence has been brought forward that inflectional and/or derivational stemmers augmented by dictionaries indeed perform substantially better than those without access to such lexical repositories ( Krovetz , 1993 ; Kraaij and Pohlmann , 1996 ; Tzoukermann et al. , 1997 ) . This result is particularly valid for natural languages with a rich morphology -- both in terms of derivation and ( single-word ) composition .
There are several grammars developed in the FB-LTAG formalism , including the XTAG English grammar , a large-scale grammar for English ( The XTAG Research Group , 2001 ) . The XTAG group ( Doran et al. , 2000 ) at the University of Pennsylvania is also developing Korean , Chinese , and Hindi grammars . Development of a large-scale French grammar ( Abeill ´ e and Candito , 2000 ) has also started at the University of Pennsylvania and is expanded at University of Paris 7 .
HOLMES is given the following set of six domainindependent rules , which are similar to the upward monotone rules introduced by ( MacCartney and Manning , 2007 ) . 1 .
The resulting approach has at its center a lexicon that partly implements current theories of lexical semantics such as Jackendoff 's ( 1990 ) and Levin 's ( 1993 ) . The lexicon is used to mediate and map between a language-independent domain model and a language-dependent ontology widely used in NLG , the Upper Model ( Bateman 1990 ) . Although the idea of a two-level representation accommodating language-neutral and language-specific requirements is not new ( see for example Nirenburg and Levin [ 1992 ] , Dorr and Voss [ 1993 ] , and Di Eugenio [ 1998 ] ) , Stede is among the few who make effective use of those two levels in a complex system .
BilderNetle ( `` little ImageNet '' in Swabian German ) is our new data set of German noun-to-ImageNet synset mappings . ImageNet is a large-scale and widely used image database , built on top of WordNet , which maps words into groups of images , called synsets ( Deng et al. , 2009 ) . Multiple synsets exist for each meaning of a word .
It crawls text/HTML files , produces an inverted file index , and assigns salience weights to terms and documents based on a simple tf-idf metric . The retrieval process relies on the vector space model ( Salton , 1989 ) , with the cosine measure expressing the similarity between a query and a document . The search engine produces a ranked output of documents .
These nodes are the left-corner ancestor of top , ( which is below top , on the stack ) , top 's left-corner child ( its leftmost child , if any ) , and top 's most recent child ( which was top , _ 1 , if any ) . For right-branching structures , the leftcorner ancestor is the parent , conditioning on which has been found to be beneficial ( Johnson , 1998 ) , as has conditioning on the left-corner child ( Roark and Johnson , 1999 ) . Because these inputs include the history features of both the leftcorner ancestor and the most recent child , a derivation step i always has access to the history features from the previous derivation step i -- 1 , and thus ( by induction ) any information from the entire previous derivation history could in principle be stored in the history features .
Most sentiment-polarity classifiers proposed in the recent literature categorize each document independently . A few others incorporate various measures of inter-document similarity between the texts to be labeled ( Agarwal and Bhattacharyya , 2005 ; Pang and Lee , 2005 ; Goldberg and Zhu , 2006 ) . Many interesting opinion-oriented documents , however , can be linked through certain relationships that occur in the context of evaluative discussions .
In capitalized-word disambiguation , however , we use this assumption with caution and first apply strategies that rely not just on single words but on words together with their local contexts ( n-grams ) . This is similar to `` one sense per collocation '' idea of Yarowsky ( 1993 ) . The description of the EAGLE workbench for linguistic engineering ( Baldwin et al. 1997 ) mentions a case normalization module that uses a heuristic in which a capitalized word in an ambiguous position should be rewritten without capitalization if it is found lower-cased in the same document .
The last decade of the 20th century saw a number of anaphora resolution projects for languages other than English such as French , German , Japanese , Spanish , Portuguese , and Turkish . Against the background of a growing interest in multilingual NLP , multilingual anaphora / coreference resolution has gained considerable momentum in recent years ( Aone and McKee 1993 ; Azzam , Humphreys , and Gaizauskas 1998 ; Harabagiu and Maiorano 2000 ; Mitkov and Barbu 2000 ; Mitkov 1999 ; Mitkov and Stys 1997 ; Mitkov , Belguith , and Stys 1998 ) . Other milestones of recent research include the deployment of probabilistic and machine learning techniques ( Aone and Bennett 1995 ; Kehler 1997 ; Ge , Hale , and Charniak 1998 ; Cardie and Wagstaff 1999 ; the continuing interest in centering , used either in original or in revised form ( Abracos and Lopes 1994 ; Strube and Hahn 1996 ; Hahn and Strube 1997 ; Tetreault 1999 ) ; and proposals related to the evaluation methodology in anaphora resolution ( Mitkov 1998a , 2001b ) .
The architectures of hierarchical semantic role classifiers can 2 Extra features e.g. predicate may be still useful because that the information , provided by the high-level description of selfdescriptive features , e.g. phrase type , are limited . be found in figure 2 , which is similar with that in Moschitti et al. ( 2005 ) . As what has been shown in figure 2 , a semantic role will first be determined whether it is a numbered argument or an ARGM by a binary-category classifier .
In this section we describe in detail the baseline NER system we use . It is inspired by the system described in Ratinov and Roth ( 2009 ) . Because NER annotations are commonly not nested ( for example , in the text `` the US Army '' , `` US Army '' is treated as a single entity , instead of the location `` US '' and the organization `` US Army '' ) it is possible to treat NER as a sequence labeling problem , where each token in the sentence receives a label which depends on which entity type it belongs to and its position in the entity .
• Only an automatic evaluation was performed , which relied on having model responses ( Berger and Mittal 2000 ; Berger et al. 2000 ) . • A user study was performed , but it was either very small compared to the corpus ( Carmel , Shtalhaim , and Soffer 2000 ; Jijkoun and de Rijke 2005 ) , or the corpus itself was significantly smaller than ours ( Feng et al. 2006 ; Leuski et al. 2006 ) . The representativeness of the sample size was not discussed in any of these studies .
The goal for query-dependent summarization algorithms is to provide a short summary of a document with respect to a specific query . Although a number of methods for query-dependent text summarization are beginning to be developed and evaluated in a variety of realistic settings ( Mani et al. , 1999 ) , we again propose the use of vector space methods from IR , which can be easily extended to the summarization task ( Salton et al. , 1994 ) : 1 .
To speed up classifiers used in a real-time application , we can build fstries incrementally by using feature vectors generated from user inputs . When we run our classifiers on resource-tight environments such as cell-phones , we can use a random feature mixing technique ( Ganchev and Dredze , 2008 ) or a memory-efficient trie implementation based on a succinct data structure ( Jacobson , 1989 ; Delpratt et al. , 2006 ) to reduce required memory usage . We will combine our method with other techniques that provide sparse solutions , for example , kernel methods on a budget ( Dekel and Singer , 2007 ; Dekel et al. , 2008 ; Orabona et al. , 2008 ) or kernel approximation ( surveyed in Kashima et al. ( 2009 ) ) .
The traditional ( First I Rest ) list notation is used , and the operator ED stands for the append relation in the usual way . 11 Manandhar ( 1995 ) proposes to unify these two steps by including an update operator in the The computational treatment we discuss in the rest of the paper follows this setup in that it automatically computes , for each lexical rule specification , the frames necessary to preserve the properties not changed by it . '
Note that these constraints can be used to license only the linguistically meaningful synchronous correspondences between the two SSTCs of the S-SSTC ( i.e. between the two languages ) . For instance , when building translation units in EBMT approaches ( Richardson et al. , 2001 ) , ( Aramaki , 2001 ) , ( AlAdhaileh & Tang , 1999 ) , ( Sato & Nagao , 1990 ) , ( Sato , 1991 ) , ( Sadler & Vendelmans , 1990 ) , etc. , where S-SSTC can be used to represent the entries of the BKB or when S-SSTC used as an annotation schema to find the translation correspondences ( lexical and structural correspondences ) for transferrules ' extraction from parallel parsed corpus ( Menezes & Richardson , 2001 ) , ( Watanabe et al. , 2000 ) , ( Meyers et al. , 2000 ) , ( Matsumoto et al. , 1993 ) and ( kaji et al. , 1992 ) .