gaof23/citation_sentiment_corpus huggingface dataset:
We carefully implemented the original Grammar Association system described in (Vidal et al. , 1993), tuned empirically a couple of smoothing parameters, trained the models and, finally, obtained an a119a21a120 a100 a104a122a121 of correct translations.9 Then, we studied the impact of: (1) sorting, as proposed in Section 3, the set of sentences presented to ECGI; (2) making language models deterministic and minimum; (3) constraining the best translation search to those sentences whose lengths have been seen, in the training set, related to the length of the input sentence.
Intuitively, if we allow any Source words to be aligned to any Target words, the best alignment that we can come up with is the one in Figure 1.c. Sentence pair (S2, T2) offers strong evidence that b c in language S means the same thing as x in language T. On the basis of this evidence, we expect the system to also learn from sentence pair (S1, T1) that a in language S means the same thing as y in language T. Unfortunately, if one works with translation models that do not allow Target words to be aligned to more than one Source word  as it is the case in the IBM models (Brown et al. , 1993)  it is impossible to learn that the phrase b c in language S means the same thing as word x in language T. The IBM Model 4 (Brown et al. , 1993), for example, converges to the word alignments shown in Figure 1.b and learns the translation probabilities shown in Figure 1.a.2 Since in the IBM model one cannot link a Target word to more than a Source word, the training procedure 2To train the IBM-4 model, we used Giza (Al-Onaizan et al. , 1999).
Using alignment for grammar and lexicon induction has been an active area of research, both in monolingual settings (van Zaanen, 2000) and in machine translation (MT) (Brown et al. , 1993; Melamed, 2000; Och and Ney, 2000) | interestingly, statistical MT techniques have been used to derive lexico-semantic mappings in the \reverse" direction of language understanding rather than generation (Papineni et al. , 1997; Macherey et al. , 2001).
Given training data consisting of parallel sentences: }1),,{( )()( Sief ii =, our Model-1 training for t(f|e) is as follows:  =  = S s ss e efefceft 1 )()(1 ),;|()|(  Where 1 e  is a normalization factor such that 0.1)|( =  j j eft ),;|( )()( ss efefc denotes the expected number of times that word e connects to word f.   == = = l i i m j jl k k ss eeff eft eft efefc 11 1 )()( ),(),( )|( )|( ),;|(  With the conditional probability t(f|e), the probability for an alignment of foreign string F given English string E is in (1):  = = + = m j n i ijm eft l EFP 1 0 )|( )1( 1 )|( (1) The probability of alignment F given E: )|( EFP is shown to achieve the global maximum under this EM framework as stated in (Brown et al. ,1993).
The relationship between the translation model and the alignment model is given by: Pr(fJ1 jeI1) = X aJ1 Pr(fJ1 ;aJ1jeI1) (3) In this paper, we use the models IBM-1, IBM4 from (Brown et al. , 1993) and the HiddenMarkovalignmentmodel(HMM)from(Vogelet al. , 1996).
Several teams had approaches that relied (to varying degrees) on an IBM model of statistical machine translation (Brown et al. , 1993), with different improvements brought by different teams, consisting of new submodels, improvements in the HMM model, model combination for optimal alignment, etc. Se-veral teams used symmetrization metrics, as introduced in (Och and Ney, 2003) (union, intersection, refined), most of the times applied on the alignments produced for the two directions sourcetarget and targetsource, but also as a way to combine different word alignment systems.
Pr(pi,F,A) = summationdisplay i,c()=(pi,F,A) productdisplay rji p(rj) (4) In order to acquire the rules specific to our model and to induce their probabilities, we parse the English side of our corpus with an in-house implementation (Soricut, 2005) of Collins parsing models (Collins, 2003) and we word-align the parallel corpus with the Giza++2 implementation of the IBM models (Brown et al. , 1993).
Nevertheless, in the problem described in this article, the source and the target sentences are given, and we are focusing on the optimization of the aligment a. The translation probability Pr(f,a|e) can be rewritten as follows: Pr(f,a|e) = Jproductdisplay j=1 Pr(fj,aj|fj11,aj11,eI1) = Jproductdisplay j=1 Pr(aj|fj11,aj11,eI1) Pr(fj|fj11,aj1,eI1) (2) The probability Pr(f,a|e) can be estimated by using the word-based IBM statistical alignment models (Brown et al. , 1993).
Monotone Nonmonotone Target B A Positions C D Source Positions Figure 1: Two Types of Alignment The IBM model 1 (IBM-1) (Brown et al. , 1993) assumes that all alignments have the same probability by using a uniform distribution: p(fJ1 |eI1) = 1IJ  Jproductdisplay j=1 Isummationdisplay i=1 p(fj|ei) (2) We use the IBM-1 to train the lexicon parameters p(f|e), the training software is GIZA++ (Och and Ney, 2003).
In the original work (Brown et al. , 1993) the posterior probability p(eI1|fJ1 ) is decomposed following a noisy-channel approach, but current stateof-the-art systems model the translation probability directly using a log-linear model(Och and Ney, 2002): p(eI1|fJ1 ) = exp parenleftBigsummationtextM m=1 mhm(e I1,fJ1 ) parenrightBig summationdisplay ?eI1 exp parenleftBigsummationtextM m=1 mhm(?eI1,fJ1 ) parenrightBig, (2) with hm different models, m scaling factors and the denominator a normalization factor that can be ignored in the maximization process.
The IBM models, together with a Hidden Markov Model (HMM), form a class of generative models that are based on a lexical translation model P(fj|ei) where each word fj in the foreign sentence fm1 is generated by precisely one word ei in the sentence el1, independently of the other translation decisions (Brown et al., 1993; Vogel et al., 1996; Och and Ney, 2000).
203 Estimating the parameters for these models is more difficult (and more computationally expensive) than with the models considered in the previous section: rather than simply being able to count the word pairs and alignment relationships and estimate the models directly, we must use an existing model to compute the expected counts for all possible alignments, and then use these counts to update the new model.7 This training strategy is referred to as expectationmaximization (EM) and is guaranteed to always improve the quality of the prior model at each iteration (Brown et al., 1993; Dempster et al., 1977).
We use these tuples to calculate a balanced f-score against the gold alignment tuples.4 Method Dict size f-score Gold 28 100.0 Monotone 39 68.9 IBM-1 (Brown et al., 1993) 30 80.3 IBM-4 (Brown et al., 1993) 29 86.9 IP 28 95.9 The last line shows an average f-score over the 8 tied IP solutions.
1 Introduction Aligning parallel texts has recently received considerable attention (Warwick et al. , 1990; Brown et al. , 1991a; Gale and Church, 1991b; Gale and Church, 1991a; Kay and Rosenschein, 1993; Simard et al. , 1992; Church, 1993; Kupiec, 1993; Matsumoto et al. , 1993).
These methods have been used in machine translation (Brown et al. , 1990; Sadler, 1989), terminology research and translation aids (Isabelle, 1992; Ogden and Gonzales, 1993), bilingual lexicography (Klavans and Tzoukermann, 1990), collocation studies (Smadja, 1992), word-sense disambiguation (Brown et al. , 1991b; Gale et al. , 1992) and information retrieval in a multilingual environment (Landauer and Littman, 1990).
Numerous experiments have shown parallel bilingual corpora to provide a rich source of constraints for statistical analysis (e.g. , Brown et al. 1990; Gale & Church 1991 ; Gale et al. 1992; Church 1993; Brown et al. 1993; Dagan et al. 1993; Fung & Church 1994; Wu & Xia 1994; Fung & McKeown 1994).
1 Introduction Despite a surge in research using parallel corpora for various machine translation tasks (Brown et al. 1993),(Brown et al. 1991; Gale & Church 1993; Church 1993; Dagan & Church 1994; Simard et al. 1992; Chen 1993; Melamed 1995; Wu & Xia 1994; Wu 1994; Smadja et aI.
Bilingual alignments have so far shown that they can play multiple roles in a wide range of linguistic applications, such as computer assisted translation (Isabelle et al. , 1993; Brown et al. , 1990), terminology (Dagan and Church, 1994) lexicography (Langlois, 1996; Klavans and Tzoukermann, 1995; Melamed, 1996), and cross-language information retrieval (Nie et al. , * This research was funded by the Canadian Department of Foreign Affairs and International Trade (http://~.dfait-maeci.gc.ca/), via the Agence de la francophonie (http://~.
However, in the experiments described here, we focus on alignment at the level of sentences, this for a number of reasons: First, sentence alignments have so far proven their usefulness in a number of applications, e.g. bilingual lexicography (Langlois, 1996; Klavans and Tzoukermann, 1995; Dagan and Church, 1994), automatic translation verification (Macklovitch, 1995; Macklovitch, 1996) and the automatic acquisition of knowledge about translation (Brown et al. , 1993).
5.1 The Prague Dependency Tree Bank (PDT in the sequel), which has been inspired by the build-up of the Penn Treebank (Marcus, Santorini & Marcinkiewicz 1993; Marcus, Kim, Marcinkiewicz et al. 1994), is aimed at a complex annotation of (a part of) the Czech National Corpus (CNC in the sequel), the creation of which is under progress at the Department of Czech National Corpus at the Faculty of Philosophy, Charles University (the corpus currently comprises about 100 million tokens of word forms).
(Carpenter, 1992), (Copestake, 1999), (DSrre and Dorna, 1993), (D6I're et al. , 1996), (Emele and Zajac, 1990), (H6ht~ld and Smolka, 1988)), and to pick those ingredients which are known to be con~i)utationally 'tractable' in some sense.
The data set consisting of 249,994 TFSs was generated by parsing the Figure 3: The size of Dpi; for the size of the data set 800 bracketed sentences in the Wall Street Journal corpus (the first 800 sentences in Wall Street Journal 00) in the Penn Treebank (Marcus et al. , 1993) with the XHPSG grammar (Tateisi et al. , 1998).
The learning algorithm used is the IB1 algorithm (Aha et al. , 1991) with k = 5, i.e. classification based on 5 nearest neighbors.4 Distances are measured using the modified value difference metric (MVDM) (Stanfill and Waltz, 1986; Cost and Salzberg, 1993) for instances with a frequency of at least 3 (and the simple overlap metric otherwise), and classification is based on distance weighted class voting with inverse distance weighting (Dudani, 1976).
For example, in the WSJ corpus, part of the Penn Treebank 3 release (Marcus et al., 1993), the string in (1) is a variation 12-gram since off is a variation nucleus that is tagged preposition (IN) in one corpus occurrence and particle (RP) in another.1 Dickinson (2005) shows that examining those cases with identical local contextin this case, lookingat ward off aresultsinanestimated error detection precision of 92.5%.
1999), OpenCCG (White, 2004) and XLE (Crouch et al., 2007), or created semi-automatically (Belz, 2007), or fully automatically extracted from annotated corpora, like the HPSG (Nakanishi et al., 2005), LFG (Cahill and van Genabith, 2006; Hogan et al., 2007) and CCG (White et al., 2007) resources derived from the Penn-II Treebank (PTB) (Marcus et al., 1993).
to estimale a model (clustering words), and measured the I(L distancd ~ between 'l'he K\], distance (relative Clt|,l:Opy), which is widely used in information theory and sta, tist, ics, is a, nleasur,2 of 'dista, n<:c' l>~\[,wcen two distributions 5.2 Experiment 2: Qualitative Evaluation We extracted roughly 180,000 case fl:anles from the bracketed WSJ (Wall Street Journal) corpus of the Penn Tree Bank (Marcus et al. , 1993) as co-occurrence data.
Treebanks have been used within the field of natural language processing as a source of training data for statistical part og speech taggers (Black et al. , 1992; Brill, 1994; Merialdo, 1994; Weischedel et al. , 1993) and for statistical parsers (Black et al. , 1993; Brill, 1993; aelinek et al. , 1994; Magerman, 1995; Magerman and Marcus, 1991).
'\['here are three main approaches in tagging problem: rule-based approach (Klein and Simmons 1%3; Brodda 1982; Paulussen and Martin 1992; Brill et al. 1990), statistical approach (Church :1988; Merialdo 1994; Foster 1991; Weischedel et al. 1993; Kupiec 1992) and connectionist approach (Benello et al. 1989; Nakanmra et al. 1989).
1 Introduction In the multilingual track of the CoNLL 2007 shared task on dependency parsing, a single parser must be trained to handle data from ten different languages: Arabic (Hajic et al. , 2004), Basque (Aduriz et al. , 2003), Catalan, (Mart et al. , 2007), Chinese (Chen et al. , 2003), Czech (Bohmova et al. , 2003), English (Marcus et al. , 1993; Johansson and Nugues, 2007), Greek (Prokopidis et al. , 2005), Hungarian (Csendes et al. , 2005), Italian (Montemagni et al. , 2003), and Turkish (Oflazer et al. , 2003).1 Our contribution is a study in multilingual parser optimization using the freely available MaltParser system, which performs 1For more information about the task and the data sets, see Nivre et al.
4 Experiments We evaluated the ISBN parser on all the languages considered in the shared task (Hajic et al. , 2004; Aduriz et al. , 2003; Mart et al. , 2007; Chen et al. , 2003; Bohmova et al. , 2003; Marcus et al. , 1993; Johansson and Nugues, 2007; Prokopidis et al. , 2005; Csendes et al. , 2005; Montemagni et al. , 2003; Oflazer et al. , 2003).
We participated in the multilingual track of the CoNLL 2007 shared task (Nivre et al. , 2007), and evaluated the system on data sets of 10 languages (Hajic et al. , 2004; Aduriz et al. , 2003; Mart et al. , 2007; Chen et al. , 2003; Bohmova et al. , 2003; Marcus et al. , 1993; Johansson and Nugues, 2007; Prokopidis et al. , 2005; Csendes et al. , 2005; Montemagni et al. , 2003; Oflazer et al. , 2003).
To train models, we used projectivized versions of the training dependency trees.2 1We are grateful to the providers of the treebanks that constituted the data for the shared task (Hajic et al. , 2004; Aduriz et al. , 2003; Mart et al. , 2007; Chen et al. , 2003; Bohmova et al. , 2003; Marcus et al. , 1993; Johansson and Nugues, 2007; Prokopidis et al. , 2005; Csendes et al. , 2005; Montemagni et al. , 2003; Oflazer et al. , 2003).
This task evaluated parsing performance on 10 languages: Arabic, Basque, Catalan, Chinese, Czech, English, Greek, Hungarian, Italian, and Turkish using data originating from a wide variety of dependency treebanks, and transformations of constituency-based treebanks (Hajic et al. , 2004; Aduriz et al. , 2003; Mart et al. , 2007; Chen et al. , 2003; Bohmova et al. , 2003; Marcus et al. , 1993; Johansson and Nugues, 2007; Prokopidis et al. , 2005; Csendes et al. , 2005; Montemagni et al. , 2003; Oflazer et al. , 2003).
In the multilingual parsing track, participants train dependency parsers using treebanks provided for ten languages: Arabic (Hajic et al. , 2004), Basque (Aduriz et al. 2003), Catalan (Mart et al. , 2007), Chinese (Chen et al. , 2003), Czech (Bhmova et al. , 2003), English (Marcus et al. , 1993; Johansson and Nugues, 2007), Greek (Prokopidis et al. , 2005), Hungarian (Czendes et al. , 2005), Italian (Montemagni et al. , 2003), and Turkish (Oflazer et al. , 2003).
In the domain adaptation track, participants were provided with English training data from the Wall Street Journal portion of the Penn Treebank (Marcus et al. , 1993) converted to dependencies (Johansson and Nugues, 2007) to train parsers to be evaluated on material in the biological (development set) and chemical (test set) domains (Kulick et al. , 2004), and optionally on text from the CHILDES database (MacWhinney, 2000; Brown, 1973).
The following treebanks were used for training the parser: (Aduriz et al. , 2003; Bhmov et al. , 2003; Chen et al. , 2003; Haji et al. , 2004; Marcus et al. , 1993; Mart et al. , 2002; Montemagni et al. 2003; Oflazer et al. , 2003; Prokopidis et al. , 2005; Csendes et al. , 2005).
We took part the Multilingual Track of all ten languages provided by the CoNLL-2007 shared task organizers(Hajic et al. , 2004; Aduriz et al. , 2003; Mart et al. , 2007; Chen et al. , 2003; Bohmova et al. , 2003; Marcus et al. , 1993; Johansson and Nugues, 2007; Prokopidis et al. , 2005; Csendes et al. , 2005; Montemagni et al. , 2003; Oflazer et al. , 2003).
5 Results and Discussion The system with online learning and Nivres parsing algorithm was trained on the data released by CoNLL Shared Task Organizers for all the ten languages (Hajic et al. , 2004; Aduriz et al. , 2003; Mart et al. , 2007; Chen et al. , 2003; Bohmova et al. , 2003; Marcus et al. , 1993; Johansson and Nugues, 2007; Prokopidis et al. , 2005; Csendes et al. , 2005; Montemagni et al. , 2003; Oflazer et al. , 2003).
3 Experimental Results and Discussion We test our parsing models on the CONLL-2007 (Hajic et al. , 2004; Aduriz et al. , 2003; Mart et al. , 2007; Chen et al. , 2003; Bohmova et al. , 2003; Marcus et al. , 1993; Johansson and Nugues, 2007; Prokopidis et al. , 2005; Csendes et al. , 2005; Montemagni et al. , 2003; Oflazer et al. , 2003) data set on various languages including Arabic, Basque, Catalan, Chinese, English, Italian, Hungarian, and Turkish.
3 Experiments and Results All experiments were conducted on the treebanks provided in the shared task (Hajic et al. , 2004; Aduriz et al. , 2003; Mart et al. , 2007; Chen et al. , 2003; Bhmov et al. , 2003; Marcus et al. , 1993; Johansson and Nugues, 2007; Prokopidis et al. , 2005; Csendes et al. , 2005; Montemagni et al. , 2003; Oflazer et al. , 2003).
In this year, CoNLL-2007 shared task (Nivre et al. , 2007) focuses on multilingual dependency parsing based on ten different languages (Hajic et al. , 2004; Aduriz et al. , 2003; Mart et al. , 2007; Chen et al. , 2003; Bhmova et al. , 2003; Marcus et al. , 1993; Johansson and Nugues, 2007; Prokopidis et al. , 2005; Czendes et al. , 2005; Montemagni et al. , 2003; Oflazer et al. , 2003) and domain adaptation for English (Marcus et al. , 1993; Johansson and Nugues, 2007; Kulick et al. , 2004; MacWhinney, 2000; Brown, 1973) without taking the languagespecific knowledge into consideration.
Set Test Set ENGLISH-WSJ Sections Section 22 Section 23 (Marcus et al., 1993) 2-21 ENGLISH-BROWN see 10% of 10% of the (Francis et al. 2002) ENGLISH-WSJ the data6 the data6 FRENCH7 Sentences Sentences Sentences (Abeille et al., 2000) 1-18,609 18,610-19,609 19,609-20,610 GERMAN Sentences Sentences Sentences (Skut et al., 1997) 1-18,602 18,603-19,602 19,603-20,602 Table 1: Corpora and standard experimental setups.
Building on the annotations from the Wall Street Journal (WSJ) portion of the Penn Treebank (Marcus et al., 1993), the project added several new layers of semantic annotations, such as coreference information, word senses, etc. In its first release (LDC2007T21) through the Linguistic Data Consortium (LDC), the project manually sense-tagged more than 40,000 examples belonging to hundreds of noun and verb types with an ITA of 90%, based on a coarse-grained sense inventory, where each word has an average of only 3.2 senses.
Our trees look just like syntactic constituency trees, such as those in the Penn TreeBank (Marcus et al., 1993), 141 ROOT PROT PROT NN PEBP2 PROT NN alpha NN A1 , , PROT NN alpha NN B1 , , CC and PROT NN alpha NN B2 NNS proteins VBD bound DT the DNA PROT NN PEBP2 NN site IN within DT the DNA NN mouse PROT NN GM-CSF NN promoter . . Figure 1: An example of our tree representation over nested named entities.
For (1), the morphemes and labels for our task are: (2) kita NEG tINC inE1S chabe VT -j SC laj PREP inA1S yol S -j SC iin PRON We also consider POS-tagging for Danish, Dutch, English, and Swedish; the English is from sections 00-05 (as training set) and 19-21 (as development set) of the Penn Treebank (Marcus et al., 1993), and the other languages are from the CoNLL-X dependency parsing shared task (Buchholz and Marsi, 2006).1 We split the original training data into training and development sets.
Corpora in various languages, such as the English Penn Treebank corpus (Marcus et al., 1993), the Swedish Stockholm-Ume corpus (Ejerhed et al., 1992), and the Icelandic Frequency Dictionary (IFD) corpus (Pind et al., 1991), have been used to train (in the case of data-driven methods) and develop (in the case of linguistic rule-based methods) different taggers, and to evaluate their accuracy, e.g.
The difference in accuracy between a SVM model applied to RRR dataset (RRR-basic experiment) and the same experiment applied to TB2 dataset (TB2278 Description Accuracy Data Extra Supervision Always noun 55.0 RRR Most likely for each P 72.19 RRR Most likely for each P 72.30 TB2 Most likely for each P 81.73 FN Average human, headwords (Ratnaparkhi et al. , 1994) 88.2 RRR Average human, whole sentence (Ratnaparkhi et al. , 1994) 93.2 RRR Maximum Likelihood-based (Hindle and Rooth, 1993) 79.7 AP Maximum entropy, words (Ratnaparkhi et al. , 1994) 77.7 RRR Maximum entropy, words & classes (Ratnaparkhi et al. , 1994) 81.6 RRR Decision trees (Ratnaparkhi et al. , 1994) 77.7 RRR Transformation-Based Learning (Brill and Resnik, 1994) 81.8 WordNet Maximum-Likelihood based (Collins and Brooks, 1995) 84.5 RRR Maximum-Likelihood based (Collins and Brooks, 1995) 86.1 TB2 Decision trees & WSD (Stetina and Nagao, 1997) 88.1 RRR WordNet Memory-based Learning (Zavrel et al. , 1997) 84.4 RRR LexSpace Maximum entropy, unsupervised (Ratnaparkhi, 1998) 81.9 Maximum entropy, supervised (Ratnaparkhi, 1998) 83.7 RRR Neural Nets (Alegre et al. , 1999) 86.0 RRR WordNet Boosting (Abney et al. , 1999) 84.4 RRR Semi-probabilistic (Pantel and Lin, 2000) 84.31 RRR Maximum entropy, ensemble (McLauchlan, 2001) 85.5 RRR LSA SVM (Vanschoenwinkel and Manderick, 2003) 84.8 RRR Nearest-neighbor (Zhao and Lin, 2004) 86.5 RRR DWS FN dataset, w/o semantic features (FN-best-no-sem) 91.79 FN PR-WWW FN dataset, w/ semantic features (FN-best-sem) 92.85 FN PR-WWW TB2 dataset, best feature set (TB2-best) 93.62 TB2 PR-WWW Table 5: Accuracy of PP-attachment ambiguity resolution (our results in bold) basic experiment) is 2.9%.
Examples are the Penn Treebank (Marcus et al. , 1993) for American English annotated at the University of Pennsylvania, the French treebank (Abeille and Clement, 1999) developed in Paris, the TIGER Corpus (Brants et al. , 2002) for German annotated at the Universities of Saarbrcurrency1ucken and This research was funded by a German Science Foundation grant (DFG SFB441-6).
This is possible because of the availability of statistical parsers, which can be trained on human-annotated treebanks (Marcus et al. , 1993; Xia et al. , 2000; Maamouri and Bies, 2004) for multiple languages; (2) The binding theory is used as a guideline and syntactic structures are encoded as features in a maximum entropy coreference system; (3) The syntactic features are evaluated on three languages: Arabic, Chinese and English (one goal is to see if features motivated by the English language can help coreference resolution in other languages).
Only recently have robust knowledge-based methods for some of these tasks begun to appear, and their performance is still not very good, as seen above in our discussion of using WordNet as a semantic network; 33 as for checking the plausibility of a hypothesis on the basis of causal knowledge about the world, we now have a much better theoretical grasp of how such inferences could be made (see, for example, Hobbs et al. \[1993\] and Lascarides and Asher \[1993\]), but we are still quite a long way from a general inference engine.
Whereas most of the work on English has been based on constituency-based representations,partly inuenced by the availability of data resources such as the Penn Treebank (Marcus,Santorini,and Marcinkiewicz 1993),it has been argued that free constituent order languages can be analyzed more adequately using dependency-based representations,which is also the kind of annotation found, for example,in the Prague Dependency Treebank of Czech (Haji c et al. 2001).