gaof23/citation_sentiment_corpus huggingface dataset:
Idiom 0 0 1 1 0 2 V. Doubt 3 0 4 0 0 7 Total A 294 160 546 39 1 1,040 In order to measure the agreement in a more precise way, we used the Kappa statistic (Siegel and Castellan 1988), recently proposed by Carletta as a measure of agreement for discourse analysis (Carletta 1996).
The two annotators agreed on the annotations of 385/453 turns, achieving 84.99% agreement, with Kappa = 0.68.2 This inter-annotator agreement exceeds that of prior studies of emotion annotation in naturally occurring speech 2a3a5a4a7a6a8a6a9a4a11a10a13a12a15a14a17a16a19a18a21a20a22a12a23a14a25a24a26a18 a27 a20a22a12a23a14a25a24a26a18 (Carletta, 1996).
The agreement on identifying the boundaries of units, using the AK statistic discussed in (Carletta, 1996), was AK BP BMBL (for two annotators and 500 units); the agreement on features(2 annotators and at least 200 units) was follows: Attribute AK Value utype .76 verbed .9 finite .81 subject .86 NPs Our instructions for identifying NP markables derive from those proposed in the MATE project scheme for annotating anaphoric relations (Poesio et al. , 1999).
On the one hand, even the higher of the kappa coefficients mentioned above is significantly lower than the standard suggested for good reliability (a124a126a125a128a127a130a129 ) or even the level where tentative conclusions may be drawn (a127a130a131a133a132a135a134a72a124 a134 a127a130a129 ) (Carletta, 1996), (Krippendorff, 1980).
This information can be annotated reliably (a1a3a2a5a4a7a6a9a8 a10a12a11a14a13a16a15 and a1a17a2a5a4a19a18a20a8 a10a12a11a14a13a16a21 ).4 4Following (Carletta, 1996), we use the a22 statistic to estimate reliability of annotation.
Analyze resulting findings to determine a progression of competence In (Michaud et al. , 2001) we discuss the initial steps we took in this process, including the development of a list of error codes documented by a coding manual, the verification of our manual and coding scheme by testing inter-coder reliability in a subset of the corpus (where we achieved a Kappa agreement score (Carletta, 1996) of a0 a1a3a2a5a4a7a6 )2, and the subsequent tagging of the entire corpus.
We measured stability (the degree to which the same annotator will produce an annotation after 6 weeks) and reproducibility (the degree to which two unrelated annotators will produce the same annotation), using the Kappa coefficient K (Siegel and Castellan, 1988; Carletta, 1996), which controls agreement P(A) for chance agreement P(E): K = PA)-P(E) 1-P(Z) Kappa is 0 for if agreement is only as would be expected by chance annotation following the same distribution as the observed distribution, and 1 for perfect agreement.
We chose nouns that occur a minimum of 10 times in the corpus, have no undetermined translations and at least five different translations in the six nonEnglish languages, and have the log likelihood score of at least 18; that is: LL(T T, T S ) =  = 2 1 ij n* j * j*i ij n log  18 where n ij stands for the number of times T T and T S have been seen together in aligned sentences, n i* and n *j stand for the number occurrences of T T and T S, respectively, and n ** represents the total 4 We computed raw percentages only; common measures of annotator agreement such as the Kappa statistic (Carletta, 1996) proved to be inappropriate for our two-category (yesno) classification scheme.
The  statistic (Carletta, 1996) is recast as: (fs,w)(sys,sys) = agr(fs,w)(sys,sys) P agr(fs,)(sys,sys) N  P agr(fs,)(sys,sys) N In this modified form, (fs,w) represents the divergence in relative agreement wrt f s for target noun w, relative to the mean relative agreement wrt f s over all words.
In this sense, instead of measuring only the categorial agreement between annotators with the kappa statistic (Carletta, 1996) or the performance of a system in terms of precision/recall, we could take into account the hierarchical organization of the categories or concepts by making use of measures considering the hierarchical distance between two concepts such as proposed by (Hahn and Schnattinger, 1998) or (Madche et al. , 2002).
The a0 coefficient is computed as follows: a0 a47 a1a32a2 a9 a1 a30 a68 a9 a1a32a30 Carletta (1996) reports that content analysis researchers generally think of a0a34a33 a49a36a35a37 as good reliability, with a49a36a35a38a40a39a37a41 a0 a41a25a49a36a35a37 allowing tentative conclusions to be drawn. All that remains is to define the chance agreement probability a1 a30 . Let a1a32a41 a1 a30 a7 and a1a32a42 a1 a30 a7 be the fraction of utterances that begin or end one or more segments in segmentation a30 respectively.
Inter-annotator agreement was assessed mainly using f-score and percentage agreement as well as 11 Table 1: Annotation examples of superlative adjectives example sup span det num car mod comp set The third-largest thrift institution in Puerto Rico also [] 22 def sg no ord 37 The Agriculture Department reported that feedlots in the 13 biggest ranch states held [] 910 def pl yes no 1112 The failed takeover would have given UAL employees 75 % voting control of the nation s second-largest airline [] 1717 pos sg no ord 1418 the kappa statistics (K), where applicable (Carletta, 1996).
Such a coding procedure covers, for example, how segmentation of a corpus is performed, if multiple tagging is allowed and if so, is it unlimited or are there just certain combinations of tags not allowed, is look ahead permitted, etc For further information on coding procedures we want to refer to \[Dybkjmr et al.1998\] and for good examples of coding books see, for example, \[Carletta et al.1996\], \[Alexandersson et al.1998\], or \[Thym~-Gobbel and Levin1998\].
S BNP VP PP VP Mr./g1820g10995 Wu/g2568 plays/g6183 basketball/g12738g10711 on/e Sunday/g7155g7411g3837 S ./g452 Figure 1 Inversion transduction Grammar parsing Any ITG can be converted to a normal form, where all productions are either lexical productions or binary-fanout nonterminal productions(Wu 1997).
Coling 2008: Companion volume  Posters and Demonstrations, pages 103106 Manchester, August 2008 Range concatenation grammars for translation Anders Sgaard University of Potsdam soegaard@ling.uni-potsdam.de Abstract Positive and bottom-up non-erasing binary range concatenation grammars (Boullier, 1998) with at most binary predicates ((2,2)-BRCGs) is a O(|G|n6) time strict extension of inversion transduction grammars (Wu, 1997) (ITGs).
Inside-out alignments (Wu, 1997), such as the one in Example 1.3, cannot be induced by any of these theories; in fact, there seems to be no useful synchronous grammar formalisms available that handle inside-out alignments, with the possible exceptions of synchronous tree-adjoining grammars (Shieber and Schabes, 1990), Bertsch and Nederhof (2001) and generalized multitext grammars (Melamed et al., 2004), which are all way more complex than ITG, STSG and (2,2)-BRCG.
Early examples of this work include (Alshawi, 1996; Wu, 1997); more recent models include (Yamada and Knight, 2001; Eisner, 2003; Melamed, 2004; Zhang and Gildea, 2005; Chiang, 2005; Quirk et al., 2005; Marcu et al., 2006; Zollmann and Venugopal, 2006; Nesson et al., 2006; Cherry, 2008; Mi et al., 2008; Shen et al., 2008).
Recently, many phrase reordering methods have been proposed, ranging from simple distancebased distortion model (Koehn  et al., 2003; Och and Ney, 2004), flat reordering model (Wu, 1997; Zens et al., 2004), lexicalized reordering model (Tillmann, 2004; Kumar and Byrne, 2005), to hierarchical phrase-based model (Chiang, 2005; Setiawan et al., 2007) and classifier-based reordering model with linear features (Zens and Ney, 2006; Xiong et al., 2006; Zhang et al., 2007a; Xiong et al., 2008).
1 Introduction Phrase-based method (Koehn et al., 2003; Och and Ney, 2004; Koehn et al., 2007) and syntaxbased method (Wu, 1997; Yamada and Knight, 2001; Eisner, 2003; Chiang, 2005; Cowan et al., 2006; Marcu et al., 2006; Liu et al., 2007; Zhang et al., 2007c, 2008a, 2008b; Shen et al., 2008; Mi and Huang, 2008) represent the state-of-the-art technologies in statistical machine translation (SMT).
S  S0,n Si,k  Si,j Sj,k Si1,i  pii Figure 1: A grammar for a large neighborhood of permutations, given one permutation pi of length n. The Si,k rules are instantiated for each 0  i < j < k  n, and the Si1,i rules for each 0 <in. We say that two permutations are neighbors iff they can be aligned by an Inversion Transduction Grammar (ITG) (Wu, 1997), which is a familiar reordering device in machine translation.
Normally, one would eliminate the redundant structures produced by the grammar in (1) by replacing it with the canonical form grammar (Wu, 1997), which has the following form: S  A | B | C A  [AB] | [BB] | [CB] | [AC] | [BC] | [CC] B  AA |BA|CA| AC |BC|CC C  e/f (2) By design, this grammar allows only one struc147 a0 a1 a2 a0 a3 a4 a2 a5 a1 a6 a7 a8 a6 a8 a9 a8 a2 a8 a10 a8 a1 a2 a3 a6 a8 a4 a7 a8 a6 a8 a9 a8 a8 a11 a12 a11 a0 a1 a2 a0 a3 a4 a2 a5 a1 a6 a7 a8 a6 a8 a9 a8 a0 a1 a2 a0 a3 a4 a2 a5 a1 a6 a7 a8 a6 a8 a9 a8 a0 a1 a2 a0 a3 a4 a2 a5 a1 a6 a7 a8 a6 a8 a9 a8 a13 a11 Figure 3: An example of how dependency trees interact with ITGs.
Concluding Remarks Formalisms for finite-state and context-free transduction have a long history (e.g. , Lewis and Stearns 1968; Aho and Ullman 1972), and such formalisms have been applied to the machine translation problem, both in the finite-state case (e.g. , Vilar et al. 1996) and the context-free case (e.g. , Wu 1997).
Recently, specific probabilistic tree-based models have been proposed not only for machine translation (Wu, 1997; Alshawi, Bangalore, and Douglas, 2000; Yamada and Knight, 2001; Gildea, 2003; Eisner, 2003), but also for This work was supported by DARPA contract F49620-001-0337 and ARDA contract MDA904-02-C-0450.
Some approaches have used syntax at the core (Wu, 1997; Alshawi et al. , 2000; Yamada and Knight, 2001; Gildea, 2003; Eisner, 2003; Hearne and Way, 2003; Melamed, 2004) while others have integrated syntax into existing phrase-based frameworks (Xia and McCord, 2004; Chiang, 2005; Collins et al. , 2005; Quirk et al. , 2005).
Then, h(s)  h(s) + Lmax, s  S. This epsilon1-admissible heuristic (Ghallab and Allard, 1982) bounds our search error by Lmax.3 3 Bitext Parsing In bitext parsing, one jointly infers a synchronous phrase structure tree over a sentence ws and its translation wt (Melamed et al. , 2004; Wu, 1997).
Other statistical machine translation systems such as (Wu, 1997) and (Alshawi et al. , 2000) also produce a tree a15 given a sentence a16 . Their models are based on mechanisms that generate two languages at the same time, so an English tree a15 is obtained as a subproduct of parsing a16 . However, their use of the LM is not mathematically motivated, since their models do not decompose into Pa4a5a2a9a8a3a10a6 and a12a14a4a5a3a7a6 unlike the noisy channel model.
Item Form: a32 a2 a49a51 a15 a52 a49 a51a16a33 Goal: a32a35a34 a49 a51 a15 a23a4a3 a12 a0a36a5 a24 a49 a51a37a33 Inference Rules Scan component d, a10a38a8 a7 a8 a0 : a39a41a40a43a42a44 a44a45 a23a25a24 a49 a5a47a46 a49 a2 a23a25a24 a5a49a48 a49 a51 a50 a23a25a24 a49 a5a47a46 a49 a20a43a5 a3a22 a23a25a24 a5a49a48 a49 a51 a51a14a52 a52 a53 a54a55 a55 a56 a23a25a24 a49 a5a47a46 a49 a2 a23a25a24 a5a49a48 a49 a51 a50 a23a25a24 a49 a5a47a46 a49a23 a19a57a24 a10a13a12 a19 a24 a23a25a24 a5a49a48 a49 a51 a58a59 a59 a60 Compose: a61a63a62a65a64 a66a68a67a69 a64 a66a71a70 a61a35a72a37a64 a66a68a67a73 a64 a66a71a70a36a74a76a75 a32a78a77 a64 a66a76a67a69 a64 a66a80a79a81a73 a64 a66 a14 a62a82a64 a66 a14 a72a37a64 a66 a33 a10 a77 a64 a66 a67a69 a64 a66a37a83 a73 a64 a66 a18 Figure 3: Logic C (C for CKY) These constraints are enforced by the d-span operators a84 and a85 . Parser C is conceptually simpler than the synchronous parsers of Wu (1997), Alshawi et al.
Variations of SCFGs go back to Aho and Ullman (1972)s Syntax-Directed Translation Schemata, but also include the Inversion Transduction Grammars in Wu (1997), which restrict grammar rules to be binary, the synchronous grammars in Chiang (2005), which use only a single nonterminal symbol, and the Multitext Grammars in Melamed (2003), which allow independent rewriting, as well as other tree-based models such as Yamada and Knight (2001) and Galley et al.
Depending on the type of input, these efforts can be divided into two broad categories: the string-based systems whose input is a string to be simultaneously parsed and translated by a synchronous grammar (Wu, 1997; Chiang, 2005; Galley et al., 2006), and the tree-based systems whose input is already a parse tree to be directly converted into a target tree or string (Lin, 2004; Ding and Palmer, 2005; Quirk et al., 2005; Liu et al., 2006; Huang et al., 2006).
Recently, many syntax-based models have been proposed to address the above deficiencies (Wu, 1997; Chiang, 2005; Eisner, 2003; Ding and Palmer, 2005; Quirk et al, 2005; Cowan et al., 2006; Zhang et al., 2007; Bod, 2007; Yamada and Knight, 2001; Liu et al., 2006; Liu et al., 2007; Gildea, 2003; Poutsma, 2000; Hearne and Way, 2003).
Null productions are also a source of double counting, as there are many possible orders in 926 N I 2+ N IN N I } N IN I I I N N N (a) Normal Domain Rules } I squigglerightN 2+ I squigglerightNI I squigglerightNI I squigglerightN N N N I I I (b) Inverted Domain Rules N 11 ,fN 11 N 11 N 10 N 10 N 10 e, N 10 N 00 } N 11 ,f  N 10 } N 10 N 00 e,  } N 00 I 11 N NI 11 N NI 00 N 00 I + 11 I 00 N 00 N 10 N 10 N 11 N N I 11 I 11 I 00 N 00 N 11 (c) Normal Domain with Null Rules } } } I 11 squiggleright ,fI 11 I 11 squigglerightI 10 I 11 squiggleright ,f  I 10 I 10 squiggleright I 10 e,  I 10 squigglerightI 00 I 10 squiggleright I 00 e,   I 00 squigglerightN + 11 N 00 I I N 00 N 11 N 11 I 00 squigglerightN 11 I I squigglerightN 11 I I squigglerightN 00 I 00 I 00 I 10 I 10 I 11 I 11 (d) Inverted Domain with Null Rules Figure 2: Illustration of two unambiguous forms of ITG grammars: In (a) and (b), we illustrate the normal grammar without nulls (presented in Wu (1997) and Zens and Ney (2003)).
The only requirement will be that a parallel corpus exist for the language under consideration and one or more other languages.2 Induction of grammars from parallel corpora is rarely viewed as a promising task in its own right; in work that has addressed the issue directly (Wu, 1997; Melamed, 2003; Melamed, 2004), the synchronous grammar is mainly viewed as instrumental in the process of improving the translation model in a noisy channel approach to statistical MT.3 In the present paper, we provide an important prerequisite for parallel corpus-based grammar induction work: an efficient algorithm for synchronous parsing of sentence pairs, given a word alignment.
(2) X1/X2    Y1:r1/Y2:r2 , [i1, j1, i2, j2], Y1/Y2   , [j1, k1, j2, k2] X1/X2   Y1:r1/Y2:r2  , [i1, k1, i2, k2] (3) X1/X2    Y1:r1/Y2:r2 , [i1, j1, j2, k2], Y1/Y2   , [j1, k1, i2, j2] X1/X2   Y1:r1/Y2:r2  , [i1, k1, i2, k2] Since each inference rule contains six free variables over string positions (i1, j1, k1, i2, j2, k2), we get a parsing complexity of order O(n6) for unlexicalized grammars (where n is the number of words in the longer of the two strings from language L1 and L2) (Wu, 1997; Melamed, 2003).
c2005 Association for Computational Linguistics Recognizing Paraphrases and Textual Entailment using Inversion Transduction Grammars Dekai Wu1 Human Language Technology Center HKUST Department of Computer Science University of Science and Technology, Clear Water Bay, Hong Kong dekai@cs.ust.hk Abstract We present first results using paraphrase as well as textual entailment data to test the language universal constraint posited by Wus (1995, 1997) Inversion Transduction Grammar (ITG) hypothesis.
The similarities become moreapparentwhenweconsiderthecanonical-form binary-bracketing ITG (Wu, 1997) shown here: S  A | B | C A  [AB] | [BB] | [CB] | [AC] | [BC] | [CC] B  AA | BA | CA | AC | BC | CC C  e/ f (3) (3) is employed in place of (2) to reduce redundant alignments and clean up EM expectations.1 More importantly for our purposes, it introduces a preterminal C, which generates all phrase pairs or cepts.
Recent work on reordering has been on trying to find smart ways to decide word order, using syntactic features such as POS tags (Lee and Ge 2005) , parse trees (Zhang et.al, 2007, Wang et.al. 2007,  Collins et.al. 2005, Yamada and Knight 2001) to name just a few, and synchronized CFG (Wu 1997, Chiang 2005), again to name just a few.
The underlying formalisms used has been quite broad and include simple formalisms such as ITGs (Wu, 1997), hierarchicalsynchronousrules(Chiang, 2005), string to tree models by (Galley et al., 2004) and (Galley et al., 2006), synchronous CFG models such (Xia and McCord, 2004) (Yamada and Knight, 2001), synchronous Lexical Functional Grammar inspired approaches (Probst et al., 2002) and others.
The production rules in ITGs are of the following form (Wu, 1997), with a notation similar to what is typically used for SDTSs and SCFGs in the right column: A  [BC] A  B1C2,B1C2 A  BC A  B1C2,C2B1 A  e | f A  e,f A  e |  A  e, A   | f A  ,f It is important to note that RHSs of production rules have at most one source-side and one targetside terminal symbol.
To overcome these limitations, many syntaxbased SMT models have been proposed (Wu, 1997; Chiang, 2007; Ding et al., 2005; Eisner, 2003; Quirk et al., 2005; Liu et al., 2007; Zhang et al., 2007; Zhang et al., 2008a; Zhang et al., 2008b; Gildea, 2003; Galley et al., 2004; Marcu et al., 2006; Bod, 2007).
2 Inversion transduction grammars Inversion transduction grammars (ITGs) (Wu, 1997) are a notational variant of binary syntax-directed translation schemas (Aho and Ullman, 1972) and are usually presented with a normal form: A  [BC] A  BC A  e|f A  e| A  |f where A,B,C  N and e,f  T. The first production rule, intuitively, says that the subtree [[]B[]C]A in the source language translates into 62 a subtree [[]B[]C]A, whereas the second production rule inverts the order in the target language, i.e. [[]C[]B]A. The universal recognition problem of ITGs can be solved in time O(n6|G|) by a CYKstyle parsing algorithm with two charts.
1 Specifically, MIMIC uses an n-dimensional call router front-end (Chu-Carroll, 2000), which is a generalization of the vector-based call-routing paradigm of semantic interpretation (Chu-CarroU and Carpenter, 1999); that is, instead of detecting one concept per utterance, MIMIC's semantic interpretation engine detects multiple (n) concepts or classes conveyed by a single utterance, by using n call touters in parallel.
2 Related Work Previous studies on entailment, inference rules, and paraphrase acquisition are roughly classified into those that require comparable corpora (Shinyama et al., 2002; Barzilay and Lee, 2003; Ibrahim et al., 2003) and those that do not (Lin and Pantel, 2001; Weeds and Weir, 2003; Geffet and Dagan, 2005; Pekar, 2006; Bhagat et al., 2007; Szpektor and Dagan, 2008).
But because we want the insertion state a1a16a20 to model digressions or unseen topics, we take the novel step of forcing its language model to be complementary to those of the other states by setting a2 a3a27a38 a21 a8 a8 a4 a8 a24 a26a11a28a30a29a6 a39a41a40a43a42a45a44a16a46 a1a48a47a1a50a49 a20 a2 a3 a26a17a21 a8a9a8 a4 a8 a24 a51a53a52a55a54a57a56 a21 a39a58a40a43a42a45a44a16a46 a1a59a47a1a50a49 a20 a2 a3a27a26a11a21a50a60 a4 a8 a24a30a24 a17 4Following Barzilay and Lee (2003), proper names, numbers and dates are (temporarily) replaced with generic tokens to help ensure that clusters contain sentences describing the same event type, rather than same actual event.
Classi er Training Set Precision Recall F-Measure Linear 10K pairs 0.837 0.774 0.804 Maximum Entropy 10K pairs 0.881 0.851 0.866 Maximum Entropy 450K pairs 0.902 0.944 0.922 Table 4: Performance of Alignment Classi er 3.2 Paraphrase Acquisition Much recent work on automatic paraphrasing (Barzilay and Lee, 2003) has used relatively simple statistical techniques to identify text passages that contain the same information from parallel corpora.
If we consider these probabilities as a vector, the similarities of two English words can be obtained by computing the dot product of their corresponding vectors.2 The formula is described below: similarity(ei, ej) = Nsummationdisplay k=1 p(ei|fk)p(ej|fk) (3) Paraphrasing methods based on monolingual parallel corpora such as (Pang et al. , 2003; Barzilay and Lee, 2003) can also be used to compute the similarity ratio of two words, but they dont have as rich training resources as the bilingual methods do.
2.2 Evaluation of Acquisition Algorithms Many methods for automatic acquisition of rules have been suggested in recent years, ranging from distributional similarity to finding shared contexts (Lin and Pantel, 2001; Ravichandran and Hovy, 2002; Shinyama et al. , 2002; Barzilay and Lee, 2003; Szpektor et al. , 2004; Sekine, 2005).
In paraphrase generation, a text unit that matches a pattern P can be rewritten using the paraphrase patterns of P. Avarietyofmethodshavebeenproposedonparaphrase patterns extraction (Lin and Pantel, 2001; Ravichandran and Hovy, 2002; Shinyama et al., 2002; Barzilay and Lee, 2003; Ibrahim et al., 2003; Pang et al., 2003; Szpektor et al., 2004).
The preci781 start Palestinian suicide bomberblew himself up in SLOT1 on SLOT2 killing SLOT3 other people and injuring wounding SLOT4 end detroit the *e* a s *e* building buildingin detroit flattened ground levelled to blasted leveled *e* was reduced razed leveled to down rubble into ashes *e* to *e* (1) (2) Figure 1: Examples of paraphrase patterns extracted by Barzilay and Lee (2003) and Pang et al.
Automatically Learning Entailment Rules from the Web Many algorithms for automatically learning paraphrases and entailment rules have been explored in recent years (Lin and Pantel, 2001; 1http://jakarta.apache.org/lucene/docs/index.html 67 Ravichandran and Hovy, 2002; Shinyama et al. , 2002; Barzilay and Lee, 2003; Sudo et al. , 2003; Szpektor et al. , 2004; Satoshi, 2005).
Most of the reported work on paraphrase generation from arbitrary input sentences uses machine learning techniques trained on sentences that are known or can be inferred to be paraphrases of each other (Bannard and Callison-Burch, 2005; Barzilay and Lee, 2003; Barzilay and McKeown, 2001; Callison-Burch et al. , 2006; Dolan et al. , 2004; Ibrahim et al. , 2003; Lin and Pantel, 2001; Pang et al. , 2003; Quirk et al. , 2004; Shinyama et al. , 2002).
2 Related Work Two different approaches have been proposed for Sentence Compression: purely statistical methodologies (Barzilay & Lee, 2003; Le Nguyen & Ho, 2004) and hybrid linguistic/statistic methodologies (Knight & Marcu, 2002; Shinyama et al. , 2002; Daelemans et al. , 2004; Marsi & Krahmer, 2005; Unno et al. , 2006).
In particular, it shows systematically better F-Measure and Accuracy measures over all other metrics showing an improvement of (1) at least 2.86% in terms of F-Measure and 3.96% in terms of Accuracy and (2) at most 6.61% in terms of FMeasure and 6.74% in terms of Accuracy compared to the second best metric which is also systematically the word N-gram overlap similarity measure used by (Barzilay & Lee, 2003).
Table 2: Figures about clustering algorithms Algorithm # Sentences/# Clusters S-HAC 6,23 C-HAC 2,17 QT 2,32 EM 4,16 In fact, table 2 shows that most of the clusters have less than 6 sentences which leads to question the results presented by (Barzilay & Lee, 2003) who only keep the clusters that contain more than 10 sentences.
Sentence Compression takes an important place for Natural Language Processing (NLP) tasks where specific constraints must be satisfied, such as length in summarization (Barzilay & Lee, 2002; Knight & Marcu, 2002; Shinyama et al. , 2002; Barzilay & Lee, 2003; Le Nguyen & Ho, 2004; Unno et al. , 2006), style in text simplification (Marsi & Krahmer, 2005) or sentence simplification for subtitling (Daelemans et al. , 2004).