hrithikpiyush/acl-arc huggingface dataset:
Shallow parsing is studied as an alternative to full-sentence parsing . Rather than producing a complete analysis of sentences , the alternative is to perform only partial analysis of the syntactic structures in a text ( Harris , 1957 ; Abney , 1991 ; Greffenstette , 1993 ) . A lot of recent work on shallow parsing has been influenced by Abney 's work ( Abney , 1991 ) , who has suggested to `` chunk '' sentences to base level phrases .
Because a tree-based gold standard for parser evaluation must adopt a particular style of linguistic analysis ( reflected in the geometry and nomenclature of the nodes in the trees ) , evaluation of statistical parsers and grammars that are derived from particular treebank resources ( as well as hand-crafted grammars/parsers ) can suffer unduly if the gold standard deviates systematically from the ( possibly ) equally valid style of linguistic analysis provided by the parser . Problems such as these have motivated research on more abstract , dependencybased parser evaluation ( e.g. , Lin 1995 ; Carroll , Briscoe , and Sanfilippo 1998 ; Carroll et al. 2002 ; Clark and Hockenmaier 2002 ; King et al. 2003 ; Preiss 2003 ; Kaplan et al. 2004 ; Miyao and Tsujii 2004 ) . Dependency-based linguistic representations are approximations of abstract predicate-argument-adjunct ( or more basic head-dependent )
Phrase tables ( PHT ) contain pairs of corresponding phrases in two languages , together with association probabilities . They are widely used in MT as a way to figure out how to translate input in one language into output in another language ( Koehn et al. , 2003 ) . There are several methods to build phrase tables .
where V is a random variable that can take any possible outcome in set V , and p ( v ) = Pr ( V = v ) is the density function . Further details about the properties of entropy can be found in textbooks on information theory ( e.g. , Cover and Thomas 1991 ) . Determining the parse tree for a sentence from a set of possible parses can be viewed as assigning a value to a random variable .
We are not aware of any empirical validation of this idea , but the fact that vague descriptions are frequent is fairly well documented . Dale and Reiter ( 1995 ) , for example , discussed the transcripts of a dialogue between people who assemble a piece of garden furniture ( originally recorded by Candy Sidner ) . They found that , while instructional texts tended to use numerical descriptions like the 31 4 '' bolt , human assemblers `` unless they were reading or discussing the written instructions , in all cases used relative modifiers , such as the long bolt '' ( Dale and Reiter 1995 ) .6 Our own experiments ( van Deemter 2004 ) point in the same direction .
Here 11 is an optimization precision , oc is a step size chosen with the strong Wolfe 's rule ( Nocedal and Wright 1999 ) . Here , PV ( A ) represents an ascent direction chosen as follows : For inequality constraints , it is the projected gradient ( Bertsekas 1999 ) ; for equality constraints with slack , we use conjugate gradient ( Nocedal and Wright 1999 ) , noting that when A = 0 , the objective is not differentiable . In practice this only happens at the start of optimization and we use a sub-gradient for the first direction .
h is a feature vector which is scaled by a weight W. Parameter estimation is one of the most important components in SMT , and various training methods have been proposed to tune W . Some methods are based on likelihood ( Och and Ney , 2002 ; Blunsom et al. , 2008 ) , error rate ( Och , 2003 ; Zhao and Chen , 2009 ; Pauls et al. , 2009 ; Galley and Quirk , 2011 ) , margin ( Watanabe et al. , 2007 ; Chiang et al. , 2008 ) and ranking ( Hopkins and May , 2011 ) , and among which minimum error rate training ( MERT ) ( Och , 2003 ) is the most popular one . All these training methods follow the same pipeline : they train only a single weight on a given development set , and then use it to translate all the sentences in a test set .
To process a pair P not encountered previously , the system builds a graph centered on the main element ( often the head ) of P . This idea was inspired by Delisle et al. ( 1993 ) , who used a list of arguments surrounding the main verb together with the verb 's subcategorization information and previously processed examples to analyse semantic roles ( case relations ) . In recent approaches , syntactic information is translated into features which , together with information from FrameNet , WordNet or VerbNet , will be used with ML tools to make predictions for each example in the test set ( Carreras and Marquez , 2004 ; Carreras and Marquez , 2005 ) .
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 . ) , `` domain circumscription '' ( cfXXX Etherington and Mercer 1987 ) , and their kin . Similarly , the notion of R + M-abduction is spiritually related to the `` abductive inference '' of Reggia ( 1985 ) , the `` diagnosis from first principles '' of Reiter ( 1987 ) , `` explainability '' of Poole ( 1988 ) , and the subset principle of Berwick ( 1986 ) .
In knowledge-lean approaches , coreference resolvers employ only morpho-syntactic cues as knowledge sources in the resolution process ( e.g. , Mitkov ( 1998 ) , Tetreault ( 2001 ) ) . While these approaches have been reasonably successful ( see Mitkov ( 2002 ) ) , Kehler et al. ( 2004 ) speculate that deeper linguistic knowledge needs to be made available to resolvers in order to reach the next level of performance . In fact , semantics plays a crucially important role in the resolution of common NPs , allowing us to identify the coreference relation between two lexically dissimilar common nouns ( e.g. , talks and negotiations ) and to eliminate George W. Bush from the list of candidate antecedents of the city , for instance .
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 ) . This result implies that parsing techniques for HPSG are also beneficial for LTAG parsing .
In total , we used 3,291 features in training the SPR . Features were discovered from the actual sentence plan trees that the SPG generated through the feature derivation process described below , in a manner similar to that used by Collins ( 2000 ) . The motivation for the features was to capture declaratively decisions made by the randomized SPG .
For example , whether a word such as `` unimaginable '' is stored in the mental lexicon as a whole word or do we break it up `` un - '' , `` imagine '' and `` able '' , understand the meaning of each of these constituent and then recombine the units to comprehend the whole word . Such questions are typically answered by designing appropriate priming experiments ( Marslen-Wilson et al. , 1994 ) or other lexical decision tasks . The reaction time of the subjects for recognizing various lexical items under appropriate conditions reveals important facts about their organization in the brain .
My theory of verbal argument structure can be implemented in a unified morpho-syntactic parsing model that interleaves syntactic and semantic parsing . The system is in the form of an agenda-driven chart-based parser whose foundation is similar to previous formalizations of Chomsky 's Minimalist Program ( Stabler , 1997 ; Harkema , 2000 ; Niyogi , 2001 ) . Lexical entries in the system are minimally specified , each consisting of a phonetic form , a list of relevant features , and semantics in the form of a A expression .
On the other hand , both believe and promise are assigned V3 which means they take a NP object and infinitival complement , yet there is a similar semantic distinction to be made between the two verbs ; so the criteria for the assignment of the V code seem to be purely syntactic . Michiels ( 1982 ) and Akkerman et al. ( 1985 ) provide a more detailed analysis of the information encoded by the LDOCE grammar codes and discuss their efficacy as a system of linguistic description . Ingria ( 1984 ) comprehensively compares different approaches to complementation within grammatical theory providing a touchstone against which the LDOCE scheme can be evaluated .
Real-time parsing imposes stringent requirements on a dictionary support environment ; at the very least it must allow frequent and rapid access to the information in the dictionary via the dictionary head words . The research described below is taking place in the context of three collaborative projects ( Boguraev , 1987 ; Russell et al. , 1986 ; Phillips and Thompson , 1986 ) to develop a general-purpose , wide coverage morphological and syntactic analyser for English . One motivation for our interest in machine readable dictionaries is to attempt to provide a substantial lexicon with lexical entries containing grammatical information compatible with the grammatical framework employed by the analyser .
In this paper we focus on the Entity Detection and Recognition task ( EDR ) for Arabic as described in ACE 2004 framework ( ACE , 2004 ) . The EDR has close ties to the named entity recognition ( NER ) and coreference resolution tasks , which have been the focus of several recent investigations ( Bikel et al. , 1997 ; Miller et al. , 1998 ; Borthwick , 1999 ; Mikheev et al. , 1999 ; Soon et al. , 2001 ; Ng and Cardie , 2002 ; Florian et al. , 2004 ) , and have been at the center of evaluations such as : MUC-6 , MUC-7 , and the CoNLL '02 and CoNLL '03 shared tasks . Usually , in computational linguistics literature , a named entity is an instance of a location , a person , or an organization , and the NER task consists of identifying each of these occurrences .
the adjective to be semantically equivalent to the superlative form ( and , analogously , the comparative ) : The n large mice = The largest n mice The large mice = The largest mice The large mouse = The largest mouse . Viewed in this way , gradable adjectives are an extreme example of the `` efficiency of language '' ( Barwise and Perry 1983 ) : Far from meaning something concrete like `` larger than 8 cm '' -- a concept that would have very limited applicability -- or even something more general like `` larger than the average N , '' a word like large is applicable across a wide range of different situations .
Overall , the driving force behind the work on learning shallow parsers was the desire to get better performance and higher reliability . However , since work in this direction has started , a significant progress has also been made in the research on statistical learning of full parsers , both in terms of accuracy and processing time ( Charniak , 1997b ; Charniak , 1997a ; Collins , 1997 ; Ratnaparkhi , 1997 ) . This paper investigates the question of whether work on shallow parsing is worthwhile .
Linguistic distance plays an important role in many applications like information retrieval , word sense disambiguation , text summarization or spelling correction . It is defined on different kinds of textual units , e.g. documents , parts of a document ( e.g. words and their surrounding context ) , words or concepts ( Lebart and Rajman , 2000 ) .2 Linguistic distance between words is inverse to their semantic similarity or relatedness . Semantic similarity is typically defined via the lexical relations of synonymy ( automobile -- car ) and hypernymy ( vehicle -- car ) , while semantic relatedness ( SR ) is defined to cover any kind of lexical or functional association that may exist be -
This will require more efficient learning algorithms and implementations . However , the greatest increase is in the amount of raw text available to be processed , e.g. the English Gigaword Corpus ( Linguistic Data Consortium , 2003 ) . Recent work ( Banko and Brill , 2001 ; Curran and Moens , 2002 ) has suggested that some tasks will benefit from using significantly more data .
Due to these limitations , instead of investigating into the synchronization of two grammars , we propose a flexible annotation schema ( i.e. Synchronous Structured String-Tree Correspondence ( S-SSTC ) ) to realize additional power and flexibility in expressing structural correspondences at the level of language sentence pairs . For example , such schema can serve as a mean to represent translation examples , or find structural correspondences for the purpose of transfer grammar learning ( Menezes & Richardson , 2001 ) , ( Aramaki et al. , 2001 ) , ( Watanabe et al. , 2000 ) , ( Meyers et al. , 2000 ) , ( Matsumoto et al. , 1993 ) , ( kaji et al. , 1992 ) , and example-base machine translation EBMT3 ( Sato & Nagao , 1990 ) , ( Sato , 1991 ) , ( Richardson et al. , 2001 ) , ( Al-Adhaileh & Tang , 1999 ) .
Och and Ney ( 2002 ) introduced the log-linear model for statistical machine translation ( SMT ) , in which translation is considered as the following optimization problem : where f and e ( e ' ) are source and target sentences , respectively .
Using additional source side information beyond the markup did not produce a gain in performance . For compound splitting , we follow Fritzinger and Fraser ( 2010 ) , using linguistic knowledge encoded in a rule-based morphological analyser and then selecting the best analysis based on the geometric mean of word part frequencies . Other approaches use less deep linguistic resources ( e.g. , POS-tags Stymne ( 2008 ) ) or are ( almost ) knowledge-free ( e.g. , Koehn and Knight ( 2003 ) ) .
2We use an additional target factor to obtain the coarse POS for each stem , applying a 7-gram POS model . Koehn and Hoang ( 2007 ) showed that the use of a POS factor only results in negligible BLEU improvements , but we need access to the POS in our inflection prediction models . step through subject-verb agreement .
The n-gram based classifier is also a naive Bayes classifier , but it operates on a different set of features . We first identified the most informative unigrams and bigrams using the information gain measure ( Yang and Pedersen 1997 ) , and then selected only the positive outcome predictors using odds ratio ( Mladenic and Grobelnik 1999 ) . Diseasespecific terms , such as rheumatoid arthritis , were then manually removed .
Zhu ( 2005 ) maintains a survey of this area . Recently , several alternative , often quite sophisticated approaches to collective classification have been proposed ( Neville and Jensen , 2000 ; Lafferty et al. , 2001 ; Getoor et al. , 2002 ; Taskar et al. , 2002 ; Taskar et al. , 2003 ; Taskar et al. , 2004 ; McCallum and Wellner , 2004 ) . It would be interesting to investigate the application of such methods to our problem .
In this work , we use the Arabic root extraction technique in ( El Kourdi , 2004 ) . It compares favorably to other stemming or root extraction algorithms ( Yates and Neto , 1999 ; Al-Shalabi and Evens , 1998 ; and Houmame , 1999 ) , with a performance of over 97 % for extracting the correct root in web documents , and it addresses the challenge of the Arabic broken plural and hollow verbs . In the remainder of this paper , we will use the term `` root '' and `` term '' interchangeably to refer to canonical forms obtained through this root extraction process .
Our results hold for bilexical context-free grammars and directly transfer to several language models that can be seen as stochastic versions of this formalism ( see Section 1 ) . We perceive that these results can be extended to other language models that properly embed bilexical context-free grammars , as for instance the more general history-based models used in ( Ratnaparkhi , 1997 ) and ( Chelba and Jelinek , 1998 ) . We leave this for future work .
( 3 ) The large mouse ( = the one whose size is 10 cm ) ( 4 ) The two large mice ( = the two whose sizes are 8 and 10 cm ) Clearly , what it takes for the adjective to be applicable has not been cast in stone , but is open to fiat : the speaker may decide that 8 cm is enough , or the speaker may set the standards higher ( cfXXX , Kennedy 1999 ) . The numeral ( whether it is implicit , as in ( 3 ) , or explicit ) can be construed as allowing the reader to draw inferences about the standards employed ( Kyburg and Morreau 2000 ; DeVault and Stone 2004 ) : ( 3 ) , for example , implies a standard that counts 10 cm as large and 8 cm as not large .
Existing text-to-speech systems perform well on word pronunciation and short sentences ,12 but when it comes to long sentences and paragraphs , synthetic speech tends to be difficult to listen to and understand . Many investigators ( e.g. Allen 1976 ; Elowitz et al. 1976 ; Luce et al. 1983 ; Cahn 1988 ) have suggested that the poor prosody of synthetic speech , in comparison with natural speech , is the primary factor leading to difficulties in the comprehension of fluent synthetic speech . And while researchers in text-tospeech synthesis have adopted a variety of approaches to prosodic phrase generation -- from the simple punctuationbased rules and function word listings of existing commercial systems to the sophisticated prosodic heuristics described in Emorine and Martin ( 1988 ) and O'Shaughnessy ( 1989 ) -- the generation of appropriate prosodic phrasing in unrestricted text has remained a problem .
In addition to question answering , multi-document summarization provides a complementary approach to addressing clinical information needs . The PERSIVAL project , the most comprehensive study of such techniques applied on medical texts to date , leverages patient records to generate personalized summaries in response to physicians ' queries ( McKeown , Elhadad , and Hatzivassiloglou 2003 ; Elhadad et al. 2005 ) . Although the system incorporates both a user and a task model , it does not explicitly capture the principles of evidence-based medicine .
Potentially this leads to a poor estimate of the phrase probabilities . See Lopez and Resnik ( 2006 ) for further discussion .
We also test an MI model inspired by Erk ( 2007 ) : We gather similar words using Lin ( 1998a ) , mining similar verbs from a comparable-sized parsed corpus , and collecting similar nouns from a broader 10 GB corpus of English text .4 We also use Keller and Lapata ( 2003 ) 's approach to obtaining web-counts . Rather than mining parse trees , this technique retrieves counts for the pattern `` V Det N '' in raw online text , where V is any inflection of the verb , Det is the , a , or the empty string , and N is the singular or plural form of the noun .
The only requirement on the problem or situation is that it can be entered into the expectation system in the form of examples . Thus , for example , it can acquire a `` script '' such as the one for going to a restaurant as defined in Schank and Abelson ( 1977 ) . The expectation module takes two inputs and produces two outputs .
These include , just to mention the most popular ones , DIRT ( Lin and Pantel , 2001 ) , VerbOcean ( Chklovski and Pantel , 2004 ) , FrameNet ( Baker et al. , 1998 ) , and Wikipedia ( Mehdad et al. , 2010 ; Kouylekov et al. , 2009 ) . DIRT is a collection of statistically learned inference rules , that is often integrated as a source of lexical paraphrases and entailment rules .
That is , the current system learns procedures rather than data structures . There is some literature on procedure acquisition such as the LISP synthesis work described in Biermann et al. ( 1984 ) and the PROLOG synthesis method of Shapiro ( 1982 ) . However , the latter methodologies have not been applied to dialogue acquisition .
The strategy decision made by the tutorial planner , together with relevant semantic content from the student 's answer ( e.g. , part of the answer to confirm ) , is passed to content planning and generation . The system uses a domain-specific content planner to produce input to the surface realizer based on the strategy decision , and a FUF/SURGE ( Elhadad and Robin , 1992 ) generation system to produce the appropriate text . Templates are used to generate some stock phrases such as `` When you are ready , go on to the next slide . ''
The lispified LDOCE file retains the broad structure of the typesetting tape and divides each entry into a number of fields -- head word , pronunciation , grammar codes , definitions , examples , and so forth . However , each of these fields requires further decoding and restructuring to provide client programs with easy access to the information they require ( see Calzolari ( 1984 ) for further discussion ) . For this purpose the formatting codes on the typesetting tape are crucial since they provide clues to the correct structure of this information .
However , the results of the cars + cameras training data combination indicate that the cameras data does not contribute any additional information during the learning , since the results on both the movies and the web-services datasets are lower than when training only on the cameras data . Our results also confirm the insights gained by Blitzer et al. ( 2007 ) , who observed that in crossdomain polarity analysis adding more training data is not always beneficial . Apparently even the smallest training dataset ( cameras ) contain enough feature instances to learn a model which performs well on the testing data .
non-attributive copyright licence to their work . We consider the Creative Commons model as the most suitable one to let each author choose the rights to reserve ( Lessig , 2004 ) . Narrative writings or essays are creative works and they generally treat ownership as authorship , even for the most enthusiastic fellows of free culture ( Stallman , 2001 ) .
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 . 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 .
It is not aimed at handling dependencies , which require heavy use of lexical information ( Hindle and Rooth , 1993 , for PP attachment ) . As ( Daelemans et al. , 1999 ) show , lexical information improves on NP and VP chunking as well . Since our method uses raw data , representing lexical entries will require a lot of memory .
We show that these PCFG-reductions result in a 60 times speedup in processing time w.r.t. Bod ( 2001 , 2003 ) . But while Bod 's estimator obtains state-of-the-art results on the WSJ , comparable to Charniak ( 2000 ) and Collins ( 2000 ) , Bonnema et al. 's estimator performs worse and is comparable to Collins ( 1996 ) . In the second part of this paper , we extend our experiments with a new notion of the best parse tree .
Another possibly critical feature is the ` mention of names ' . In multi-party discussion people usually mention each other 's name for the purpose of disentanglement ( Elsner and Charniak , 2008 ) . In our corpus we found 175 instances where a participant mentions other participant 's name .
Models of translational equivalence that are ignorant of indirect associations have `` a tendency ... to be confused by collocates '' ( Dagan et al. , 1993 ) . Fortunately , indirect associations are usually not difficult to identify , because they tend to be weaker than the direct associations on which they are based ( Melamed , 1996c ) . The majority of indirect associations can be filtered out by a simple competition heuristic : Whenever several word tokens ui in one half of the bitext co-occur with a particular word token v in the other half of the bitext , the word that is most likely to be v 's translation is the one for which the likelihood L ( u , v ) of translational equivalence is highest .