gaof23/citation_sentiment_corpus huggingface dataset:
To our knowledge no systems directly address Problem 1, instead choosing to ignore the problem by using one or a small handful of reference derivations in an n-best list (Liang et al., 2006; Watanabe et al., 2007), or else making local independence assumptions which side-step the issue (Ittycheriah and Roukos, 2007; Tillmann and Zhang, 2007; Wellington et al., 2006).
In general, Agold / Acandidates; following (Collins, 2000) and (Charniak and Johnson, 2005) for parse reranking and (Liang et al., 2006) for translation reranking, we define Aoracle as alignment in Acandidates that is most similar to Agold.8 We update each feature weight i as follows: i = i + hAoraclei hA1-besti .9 Following (Moore, 2005), after each training pass, we average all the feature weight vectors seen during the pass, and decode the discriminative training set using the vector of averaged feature weights.
Perhaps more importantly, discriminative models have been shown to offer competitive performance on a variety of sequential and structured learning tasks in NLP that are traditionally tackled via generative models , such as letter-to-phoneme conversion (Jiampojamarn et al., 2008), semantic role labeling (Toutanova et al., 2005), syntactic parsing (Taskar et al., 2004), language modeling (Roark et al., 2004), and machine translation (Liang et al., 2006).
We then perform the D-step following (Fraser and A B C D d110d110d110d110d110 d110d110d110d110d110 d110d110d110d110 E d64d64d64 d64d64d64 d64 d126d126d126 d126d126d126 d126 A B C D d110d110d110d110d110 d110d110d110d110d110 d110d110d110d110 E d64d64d64 d64d64d64 d64 d126d126d126 d126d126d126 d126 Figure 2: Two alignments with the same translational correspondence Marcu, 2006b).
F-Measure with an appropriate setting of  will be useful during the development process of new alignment models, or as a maximization criterion for discriminative training of alignment models (Cherry and Lin 2003; Ayan, Dorr, and Monz 2005; Ittycheriah and Roukos 2005; Liu, Liu, and Lin 2005; Fraser and Marcu 2006; Lacoste-Julien et al. 2006; Moore, Yih, and Bode 2006).
Method Prec Rec F-measure GIZA++ Intersect 96.7 53.0 68.5 GIZA++ Union 82.5 69.0 75.1 GIZA++ GDF 84.0 68.2 75.2 Phrasal ITG 50.7 80.3 62.2 Phrasal ITG + NCC 75.4 78.0 76.7 Following the lead of (Fraser and Marcu, 2006), we hand-aligned the first 100 sentence pairs of our training set according to the Blinker annotation guidelines (Melamed, 1998).
Although some early systems for web-page analysis induce rules at character-level (e.g., such as WIEN (Kushmerick et al., 1997) and DIPRE (Brin, 1998)), most recent approaches for set expansion have used either tokenized and/or parsed free-text (Carlson et al., 2009; Talukdar et al., 2006; Snow et al., 2006; Pantel and Pennacchiotti, 2006), or have incorporated heuristics for exploiting HTML structures that are likely to encode lists and tables (Nadeau et al., 2006; Etzioni et al., 2005).
1510 5 Related Work In recent years, many research has been done on extracting relations from free text (e.g., (Pantel and Pennacchiotti, 2006; Agichtein and Gravano, 2000; Snow et al., 2006)); however, almost all of them require some language-dependent parsers or taggers for English, which restrict the language of their extractions to English only (or languages that have these parsers).
Finally, methods in the literature more focused on a specific disambiguation task include statistical methods for the attachment of hyponyms under the most likely hypernym in the WordNet taxonomy (Snow et al., 2006), structural approaches based on semantic clusters and distance metrics (Pennacchiotti and Pantel, 2006), supervised machine learning methods for the disambiguation of meronymy relations (Girju et al., 2003), etc. 6 Conclusions In this paper we presented a novel approach to disambiguate the glosses of computational lexicons and machine-readable dictionaries, with the aim of alleviating the knowledge acquisition bottleneck.
4 Related Work 4.1 Acquisition of Classes of Instances Although some researchers focus on re-organizing or extending classes of instances already available explicitly within manually-built resources such as Wikipedia (Ponzetto and Strube, 2007) or WordNet (Snow et al., 2006) or both (Suchanek et al., 2007), a large body of previous work focuses on compiling sets of instances, not necessarily labeled, from unstructured text.
(Ponzetto and Strube, 2007; Snow et al., 2006)), can be summarized as: [] C [such as|including] I [and|,|.], where I is a potential instance (e.g., Venezuelan equine encephalitis) and C is a potential class label for the instance (e.g., zoonotic diseases), for example in the sentence: The expansion of the farms increased the spread of zoonotic diseases such as Venezuelan equine encephalitis [].
2.1 Relationship Types There is a large body of related work that deals with discovery of basic relationship types represented in useful resources such as WordNet, including hypernymy (Hearst, 1992; Pantel et al., 2004; Snow et al., 2006), synonymy (Davidov and Rappoport, 2006; Widdows and Dorow, 2002) and meronymy (Berland and Charniak, 1999; Girju et al., 2006).
Currently, the best-performing English NP interpretation methods in computational linguistics focus mostly on two consecutive noun instances (noun compounds) and are either (weakly) supervised, knowledge-intensive (Rosario and Hearst, 2001), (Rosario et al. , 2002), (Moldovan et al. , 2004), (Pantel and Pennacchiotti, 2006), (Pennacchiotti and Pantel, 2006), (Kim and Baldwin, 2006), (Snow et al. , 2006), (Girju et al. , 2005; Girju et al. , 2006), or use statistical models on large collections of unlabeled data (Berland and Charniak, 1999), (Lapata and Keller, 2004), (Nakov and Hearst, 2005), (Turney, 2006).
This increase of probabilities is defined as multiplicative change (N) as follows: (N) = P(E|Tprime)/P(E|T) (2) The main innovation of the model in (Snow et al., 2006) is the possibility of adding at each step the best relation N = {Ri,j}as well as N = I(Ri,j) that is Ri,j with all the relations by the existing taxonomy.
The up-arrows and down-arrows are shorthand for (M(ni)) = (ni) where ni is the c-structure node annotated with the equation.2 Treebest := argmaxTreeP(Tree|F-Str) (1) P(Tree|F-Str) := productdisplay X  Y in Tree Feats = {ai|vj((X))ai = vj} P(X  Y |X, Feats) (2) The generation model of (Cahill and van Genabith, 2006) maximises the probability of a tree given an f-structure (Eqn.
F-Struct Feats Grammar Rules {PRED=PRO,NUM=SG PER=3, GEN=FEM} PRP-nom(=)  she {PRED=PRO,NUM=SG PER=3, GEN=FEM} PRP-acc(=)  her Table 5: Lexical item rules with case markings 4 A History-Based Generation Model The automatic generation grammar transform presented in (Cahill and van Genabith, 2006) provides a solution to coarse-grained and (in fact) inappropriate independence assumptions in the basic generation model.
Note, that for our example the effect of the uniform additional conditioning on mother grammatical function has the same effect as the generation grammar transform of (Cahill and van Genabith, 2006), but without the need for the gramF-Struct Feats Grammar Rules {PRED=PRO,NUM=SG PER=3, GEN=FEM, SUBJ} PRP(=)  she {PRED=PRO,NUM=SG PER=3, GEN=FEM, OBJ} PRP(=)  her Table 7: Lexical item rules.
Existing statistical NLG (i) uses corpus statistics to inform heuristic decisions in what is otherwise symbolic generation (Varges and Mellish, 2001; White, 2004; Paiva and Evans, 2005); (ii) applies n-gram models to select the overall most likely realisation after generation (HALOGEN family); or (iii) reuses an existing parsing grammar or treebank for surface realisation (Velldal et al. , 2004; Cahill and van Genabith, 2006).
c2009 Association for Computational Linguistics Automatic Treebank-Based Acquisition of Arabic LFG Dependency Structures Lamia Tounsi Mohammed Attia NCLT, School of Computing, Dublin City University, Ireland {lamia.tounsi, mattia, josef}@computing.dcu.ie Josef van Genabith Abstract A number of papers have reported on methods for the automatic acquisition of large-scale, probabilistic LFG-based grammatical resources from treebanks for English (Cahill and al., 2002), (Cahill and al., 2004), German (Cahill and al., 2003), Chinese (Burke, 2004), (Guo and al., 2007), Spanish (ODonovan, 2004), (Chrupala and van Genabith, 2006) and French (Schluter and van Genabith, 2008).
The word alignment used in GHKM is usually computed independent ofthesyntacticstructure,andasDeNeroandKlein (2007) and May and Knight (2007) have noted, Ch-En En-Ch Union Heuristic 28.6% 33.0% 45.9% 20.1% Table 1: Percentage of corpus used to generate big templates, based on different word alignments 9-12 13-20 21 Ch-En 18.2% 17.4% 64.4% En-Ch 15.9% 20.7% 63.4% Union 9.8% 15.1% 75.1% Heuristic 24.6% 27.9% 47.5% Table 2: In the selected big templates, the distribution of words in the templates of different sizes, which are measured based on the number of symbols in their RHSs is not the best for SSMT systems.
These include cube pruning (Chiang, 2007), cube growing (Huang and Chiang, 2007), early pruning (Moore and Quirk, 2007), closing spans (Roark and Hollingshead, 2008; Roark and Hollingshead, 2009), coarse-to-fine methods (Petrov et al., 2008), pervasive laziness (Pust and Knight, 2009), and many more.
Recall that the log likelihood of our model is:  d parenleftBigg Lorig(Dd;d) i (d,i ,i)2 2 2d parenrightBigg  i (,i)2 2 2 We now introduce a new variable d = d , and plug it into the equation for log likelihood:  d parenleftBigg Lorig(Dd;d +) i (d,i)2 2 2d parenrightBigg  i (,i)2 2 2 The result is the model of (Daume III, 2007), where the d are the domain-specific feature weights, and d are the domain-independent feature weights.
Differences in behavior of WSD systems when applied to lexical-sample and all-words datasets have been observed on previous Senseval and Semeval competitions (Kilgarriff, 2001; Mihalcea et al., 2004; Pradhan et al., 2007): supervised systems attain results on the high 80s and beat the most frequent baseline by a large margin for lexical-sample datasets, but results on the all-words datasets were much more modest, on the low 70s, and a few points above the most frequent baseline.
73 ID Participant BBN-COMBO BBN system combination (Rosti et al., 2008) CMU-COMBO Carnegie Mellon University system combination (Jayaraman and Lavie, 2005) CMU-GIMPEL Carnegie Mellon University Gimpel (Gimpel and Smith, 2008) CMU-SMT Carnegie Mellon University SMT (Bach et al., 2008) CMU-STATXFER Carnegie Mellon University Stat-XFER (Hanneman et al., 2008) CU-TECTOMT Charles University TectoMT (Zabokrtsky et al., 2008) CU-BOJAR Charles University Bojar (Bojar and Hajic, 2008) CUED Cambridge University (Blackwood et al., 2008) DCU Dublin City University (Tinsley et al., 2008) LIMSI LIMSI (Dechelotte et al., 2008) LIU Linkoping University (Stymne et al., 2008) LIUM-SYSTRAN LIUM / Systran (Schwenk et al., 2008) MLOGIC Morphologic (Novak et al., 2008) PCT a commercial MT provider from the Czech Republic RBMT16 Babelfish, Lingenio, Lucy, OpenLogos, ProMT, SDL (ordering anonymized) SAAR University of Saarbruecken (Eisele et al., 2008) SYSTRAN Systran (Dugast et al., 2008) UCB University of California at Berkeley (Nakov, 2008) UCL University College London (Wang and Shawe-Taylor, 2008) UEDIN University of Edinburgh (Koehn et al., 2008) UEDIN-COMBO University of Edinburgh system combination (Josh Schroeder) UMD University of Maryland (Dyer, 2007) UPC Universitat Politecnica de Catalunya, Barcelona (Khalilov et al., 2008) UW University of Washington (Axelrod et al., 2008) XEROX Xerox Research Centre Europe (Nikoulina and Dymetman, 2008) Table 2: Participants in the shared translation task.
Another important direction is classifying sentences as subjective or objective, and classifying subjective sentences or clauses as positive or negative (Wiebe et al, 1999; Wiebe and Wilson, 2002, Yu and Hatzivassiloglou, 2003; Wilson et al, 2004; Kim and Hovy, 2004; Riloff and Wiebe, 2005; Gamon et al 2005; McDonald et al, 2007).
Several researchers also studied feature/topicbased sentiment analysis (e.g., Hu and Liu, 2004; Popescu and Etzioni, 2005; Ku et al, 2006; Carenini et al, 2006; Mei et al, 2007; Ding, Liu and Yu, 2008; Titov and R. McDonald, 2008; Stoyanov and Cardie, 2008; Lu and Zhai, 2008).
Various machine learning strategies have been proposed to address this problem, including semi-supervised learning (Zhu, 2007), domain adaptation (Wu and Dietterich, 2004; Blitzer et al., 2006; Blitzer et al., 2007; Arnold et al., 2007; Chan and Ng, 2007; Daume, 2007; Jiang and Zhai, 2007; Reichart and Rappoport, 2007; Andreevskaia and Bergler, 2008), multi-task learning (Caruana, 1997; Reichart et al., 2008; Arnold et al., 2008), self-taught learning (Raina et al., 2007), etc. A commonality among these methods is that they all require the training data and test data to be in the same feature space.
5.3 Analysis of BF-LM framework We refer to (Talbot and Osborne, 2007) for empirical results establishing the performance of the logfrequency BF-LM: overestimation errors occur with 474 0.01 0.025 0.05 0.1 0.25 0.5 0.03 0.02 0.01 0.005 0.0025 0.001 Mean squared error of log probabilites Memory in GB MSE between WB 3-gram SRILM and BF-LMs Base 3 Base 1.5 Base 1.1 Figure 5: MSE between SRILM and BF-LMs 22 23 24 25 26 27 28 29 30 0.01 0.1 1 BLEU Score Mean squared error WB-smoothed BF-LM 3-gram model BF-LM base 1.1 BF-LM base 1.5 BF-LM base 3 Figure 6: MSE vs. BLEU for WB 3-gram BF-LMs a probability that decays exponentially in the size of the overestimation error.
3 Language modelling with Bloom filters Recentwork(TalbotandOsborne, 2007)presenteda scheme for associating static frequency information with a set of n-grams in a BF efficiently.1 3.1 Log-frequency Bloom filter The efficiency of the scheme for storing n-gram statistics within a BF presented in Talbot and Osborne (2007) relies on the Zipf-like distribution of n-gramfrequencies: mosteventsoccuranextremely small number of times, while a small number are very frequent.
This curve plots the average labeled attachment score over Basque, Chinese, English, and Turkish as a function of parsing time per token.4 Accuracy of only 1% below the maximum can be achieved with average processing time of 17 ms per token, or 60 tokens per second.5 We also refer the reader to (Titov and Henderson, 2007b) for more detailed analysis of the ISBN dependency parser results, where, among other things, it was shown that the ISBN model is especially accurate at modeling long dependencies.
For English, after a relatively big jump achieved by (Collins, 2002), we have seen two significant improvements: (Toutanova et al., 2003) and (Shen et al., 2007) pushed the results by a significant amount each time.1 1In our final comparison, we have also included the results of (Gimenez and M`arquez, 2004), because it has surpassed (Collins, 2002) as well and we have used this tagger in the data preparation phase.
3 The data 3.1 The supervised data For English, we use the same data division of Penn Treebank (PTB) parsed section (Marcus et al., 1994) as all of (Collins, 2002), (Toutanova et al., 2003), (Gimenez and M`arquez, 2004) and (Shen et al., 2007) do; for details, see Table 1.
3.5 Regularization We apply lscript1 regularization (Ng, 2004; Gao et al., 2007) to make learning more robust to noise and control the effective dimensionality of the feature spacebysubtractingaweightedsumofabsolutevalues of parameter weights from the log-likelihood of the training data w = argmaxw LL(w) summationdisplay i Ci|wi| (6) We optimize the objective using a variant of the orthant-wise limited-memory quasi-Newton algorithm proposed by Andrew & Gao (2007).3 All values Ci are set to 1 in most of the experiments below, although we apply stronger regularization (Ci = 3) to reordering features.
Salience Feature Pronoun Name Nominal TOP 0.75 0.17 0.08 HIGH 0.55 0.28 0.17 MID 0.39 0.40 0.21 LOW 0.20 0.45 0.35 NONE 0.00 0.88 0.12 Table 2: Posterior distribution of mention type given salience (taken from Haghighi and Klein (2007)) 3.3 Modifications to the H&K Model Next, we discuss the potential weaknesses of H&Ks model and propose three modifications to it.
1153 While much research (Ng and Cardie, 2002; Culotta et al., 2007; Haghighi and Klein, 2007; Poon and Domingos, 2008; Finkel and Manning, 2008) has explored how to reconcile pairwise decisions to form coherent clusters, we simply take the transitive closure of our pairwise decision (as in Ng and Cardie (2002) and Bengston and Roth (2008)) which can and does cause system errors.
2 Related Work Given its potential usefulness in coreference resolution, anaphoricity determination has been studied fairly extensively in the literature and can be classified into three categories: heuristic rule-based (e.g. Paice and Husk 1987; Lappin and Leass 1994; Kennedy and Boguraev 1996; Denber 1998; Vieira and Poesio 2000), statistics-based (e.g., Bean and Riloff 1999; Cherry and Bergsma 2005; Bergsma et al 2008) and learning-based (e.g. Evans 2001; Ng and Cardie 2002a; Ng 2004; Yang et al 2005; Denis and Balbridge 2007).
For example, aspects of a digital camera could include picture quality, battery life, size, color, value, etc. Finding such aspects is a challenging research problem that has been addressed in a number of ways (Hu and Liu, 2004b; Gamon et al., 2005; Carenini et al., 2005; Zhuang et al., 2006; Branavan et al., 2008; Blair-Goldensohn et al., 2008; Titov and McDonald, 2008b; Titov and McDonald, 2008a).
Pearsons correlation coefficient is a standard measure of the correlation strength between two distributions; it can be calculated as follows:  = E(XY ) E(X)E(Y )radicalbigE(X2)  [E(X)]2radicalbigE(Y 2)  [E(Y )]2 (1) where X = (x1,,xn) and Y = (y1,,yn) are vectors of numerical scores for each paraphrase provided by the humans and the competing systems, respectively, n is the number of paraphrases to score, and E(X) is the expectation of X. Cosine correlation coefficient is another popular alternative and was used by Nakov and Hearst (2008); it can be seen as an uncentered version of Pearsons correlation coefficient:  = X.YbardblXbardblbardblYbardbl (2) Spearmans rank correlation coefficient is suitable for comparing rankings of sets of items; it is a special case of Pearsons correlation, derived by considering rank indices (1,2,) as item scores . It is defined as follows:  = n summationtextx iyi  ( summationtextx i)( summationtexty i)radicalBig nsummationtextx2i  (summationtextxi)2 radicalBig nsummationtexty2i  (summationtextyi)2 (3) One problem with using Spearmans rank coefficient for the current task is the assumption that swapping any two ranks has the same effect.
Audio data amenable to summarization include meeting recordings (Murray et al., 2005), telephone conversations (Zhu and Penn, 2006; Zechner, 2001), news broadcasts (Maskey and Hirschberg, 2005; Christensen et al., 2004), presentations (He et al., 2000; Zhang et al., 2007; Penn and Zhu, 2008), etc. Although extractive summarization is not as ideal as abstractive summarization, it outperforms several comparable alternatives.
They can be roughly divided into three categories: string-to-tree models (e.g., (Galley et al., 2006; Marcu et al., 2006; Shen et al., 2008)), tree-to-string models (e.g., (Liu et al., 2006; Huang et al., 2006)), and tree-totree models (e.g., (Eisner, 2003; Ding and Palmer, 2005; Cowan et al., 2006; Zhang et al., 2008)).
For instance, some approaches coarsely discriminate between biographical and non-biographical information (Zhou et al., 2004; Biadsyetal.,2008),whileothersgobeyondbinary distinction by identifying atomic events  e.g., occupation and marital status  that are typically included in a biography (Weischedel et al., 2004; Filatova and Prager, 2005; Filatova et al., 2006).
Many NLP systems use the output of supervised parsers (e.g., (Kwok et al., 2001) for QA, (Moldovan et al., 2003) for IE, (Punyakanok et al., 2008) for SRL, (Srikumar et al., 2008) for Textual Inference and (Avramidis and Koehn, 2008) for MT).
(1) a) ~ x e' ~ y ?read(e' x y) & book(y) b) ~ x 3 e e' y past(e) & enjoy(e x e') & ?read(e' x y) & book(y) c) 3 e e' y past(e) & enjoy(e j e') & ?read(e' j y) & book(y) We follow Hobbs (1985), Alshawi et al.
A subst(req, cons(c, argo)) st ^ rel(c, z) s2 ~(i,k,=,;~z\[p~(:) ^ ~(~)\]) (Vi,j,w)n(i,j,w) D (3z)cn(i,j,z,w) (Vi,j, k, w, z, c, rel)prep(i, j, w) ^ np(j, k, x) A rel(c, z) In 3 ptXi, k,,~z\[w(c, z)\], <c>, Req(w)) For example, the first axiom says that there is a sentence from point i to point k asserting eventuality e if there is a noun phrase from i to j referring to z and a verb phrase from j to k denoting predicate p with arguments arg8 and having an associated requirement req, and there is (or, for $3, can be assumed to be) an eventuality e of p's being true of , where c is related to or coercible from x (with an assumability cost of $20), and the requirement req associated with p can be proved or, for $10, assumed to hold of the arguments of p. The symbol c&el denotes the conjunction of eventualities e and el (See Hobbs (1985b), p. 35).
1.2.2 SPECIFIC SYNTACTIC AND SEMANTIC ASSUMPTIONS The basic scheme, or some not too distant relative, is the one used in many large-scale implemented systems; as examples, we can quote TEAM (Grosz et al. 1987), PUNDIT (Dahl et al. 1987), TACITUS (Hobbs et al. 1988), MODL (McCord 1987), CLE (Alshawi et al. 1989), and SNACK-85 (Rayner and Banks 1986).
Volume 17, Number 1 March 1991 References Lakoff, George and Johnson, Mark Metaphors We Live 8y University of Chicago Press 1980 MADCOW Committee (Hirschman, Lynette et al) Multi-Site Data Collection for a Spoken Language Corpus in Proceedings Speech and Natural Language Workshop February 1992 Grice, H. P. Logic and Conversation in P. Cole and J. L. Morgan, Speech Acts, New York: Academic Press, 1975 Pustejovsky, James The Generative Lexicon Computational Linguistics Volume 17, Number 4 December 1991 Hobbs, Jerry R. and Stickel, Mark Interpretation as Abduction in Proceedings of the 26th ACL June 1988 Bobrow, R. , Ingria, R. and Stallard, D. The Mapping Unit Approach to Subcategorization in Proceedings Speech and Natural Language Workshop February 1991 Hobbs, Jerry R. , and Martin, Paul Local Pragmatics in Proceedings, 10th International Joint Conference on Artificial Intelligence (IJCAI-87).
Among the applications of collocational analysis for lexical acquisition are: the derivation of syntactic disambiguation cues (Basili et al. 1991, 1993a; Hindle and Rooths 1991,1993; Sekine 1992) (Bogges et al. 1992), sense preference (Yarowski 1992), acquisition of selectional restrictions (Basili et al. 1992b, 1993b; Utsuro et al. 1993), lexical preference in generation (Smadjia 1991), word clustering (Pereira 1993; Hindle 1990; Basili et al. 1993c), etc. In the majority of these papers, even though the (precedent or subsequent) statistical processing reduces the number of accidental associations, very large corpora (10,000,000 words) are necessary to obtain reliable data on a "large enough" number of words.
Semantic collocations are harder to extract than cooccurrence patterns--the state of the art does not enable us to find semantic collocations automatically t. This paper however argues that if we take advantage of lexicai paradigmatic behavior underlying the lexicon, we can at least achieve semi-automatic extraction of semantic collocations (see also Calzolari and Bindi (1990) I But note the important work by Hindle \[HindlegO\] on extracting semantically similar nouns based on their substitutability in certain verb contexts.
We use the cosine similarity measure for windowbased contexts and the following commonly used similarity measures for the syntactic vector space: Hindles (1990) measure, the weighted Lin measure (Wu and Zhou, 2003), the -Skew divergence measure (Lee, 1999), the Jensen-Shannon (JS) divergence measure (Lin, 1991), Jaccards coef cient (van Rijsbergen, 1979) and the Confusion probability (Essen and Steinbiss, 1992).