gaof23/citation_sentiment_corpus huggingface dataset:
a22 a14 is the sufficient statistic of a16 a14 . Then, we can rewrite a2a24a3 a10a27 a42a7 a25 as: a5a7a6a9a8a11a10 a23 a3 a10 a7 a15 a27 a25a18a17a26a25 a12a28a27 a5a7a6a29a8a30a10 a23 a3 a10 a7 a15 a27 a25a18a17 . 3 Loss Functions for Label Sequences Given the theoretical advantages of discriminative models over generative models and the empirical support by (Klein and Manning, 2002), and that CRFs are the state-of-the-art among discriminative models for label sequences, we chose CRFs as our model, and trained by optimizing various objective functions a31 a3 a10a36 a25 with respect to the corpus a36 . The application of these models to the label sequence problems vary widely.
Automatic subjectivity analysis would also be useful to perform flame recognition (Spertus 1997; Kaufer 2000), e-mail classification (Aone, Ramos-Santacruze, and Niehaus 2000), intellectual attribution in text (Teufel and Moens 2000), recognition of speaker role in radio broadcasts (Barzialy et al. 2000), review mining (Terveen et al. 1997), review classification (Turney 2002; Pang, Lee, and Vaithyanathan 2002), style in generation (Hovy 1987), and clustering documents by ideological point of view (Sack 1995).
Workshop Towards Genre-Enabled Search Engines</booktitle> <pages>13--20</pages> <editor>In G. Rehm and M. Santini, editors</editor> <contexts> <context>ork on an intra-document, or page segment level because a single document can contain instances of multiple genres, e.g., contact information, list of publications, C.V., see (Rehm, 2002; Rehm, 2007; Mehler et al., 2007).
So far research in automatic opinion recognition has primarily addressed learning subjective language (Wiebe et al. , 2004; Riloff et al. , 2003; Riloff and Wiebe, 2003), identifying opinionated documents (Yu and Hatzivassiloglou, 2003) and sentences (Yu and Hatzivassiloglou, 2003; Riloff et al. , 2003; Riloff and Wiebe, 2003), and discriminating between positive and negative language (Yu and Hatzivassiloglou, 2003; Turney and Littman, 2003; Pang et al. , 2002; Dave et al. , 2003; Nasukawa and Yi, 2003; Morinaga et al. , 2002).
There are studies on learning subjective language (Wiebe et al. , 2004), identifying opinionated documents (Yu and Hatzivassiloglou, 2003) and sentences (Riloff et al. , 2003; Riloff and Wiebe, 2003), and discriminating between positive and negative language (Turney and Littman, 2003; Pang et al. , 2002; Dave et al. , 2003; Nasukawa and Yi, 2003; Morinaga et al. , 2002).
Among these methods, SVM is shown to perform better than other methods (Yang and Pedersen, 1997; Pang et al.,  1 http://people.csail.mit.edu/~jrennie/20Newsgroups/ 2 http://www.cs.cornell.edu/People/pabo/movie-review-data/ 3 http://www.seas.upenn.edu/~mdredze/datasets/sentiment/  2002).
So far, research in automatic opinion recognition has primarily addressed learning subjective language (Wiebe et al. , 2004; Riloff et al. , 2003), identifying opinionated documents (Yu and Hatzivassiloglou, 2003) and sentences (Yu and Hatzivassiloglou, 2003; Riloff et al. , 2003), and discriminating between positive and negative language (Pang et al. , 2002; Morinaga et al. , 2002; Yu and Hatzivassiloglou, 2003; Turney and Littman, 2003; Dave et al. , 2003; Nasukawa and Yi, 2003; Popescu and Etzioni, 2005; Wilson et al. , 2005).
Research on the automatic classification of movie or product reviews as positive or negative (e.g. , (Pang et al. , 2002; Morinaga et al. , 2002; Turney and Littman, 2003; Nasukawa and Yi, 2003; Mullen and Collier, 2004; Beineke et al. , 2004; Hu and Liu, 2004)) is perhaps the most similar to our work.
WLCS(w,d) =summationtextmi=0 f(ki) We then compute the following quantities, where || is word length, and f1 is the inverse of f. P(w,d) = f1(WLCS(w,d)f(|w|) ) R(w,d) = f1(WLCS(w,d)f(|d|) ) F(w,d) = (1+2)R(w,d)P(w,d)R(w,d)+2P(w,d) In effect, P(w,d) examines how close the longest common substring is to w and R(w,d) how close it is to d. Following Lin (2004), we use  = 8, assigninggreaterimportancetoR(w,d).
ROUGE-N ROUGE-N is an N-gram-based evaluation measure defined as follows (Lin, 2004b): ROUGE-Na59a61a146a31a62a90a147a49a65a68a67 a215 a77a83a216 a209a68a217a61a173 a172a27a218 a77 a215 a219a27a220a158a221a183a222a85a223 a172 a173a78a224a76a225a164a226 a59a136a227a158a228 a152a130a150a104a229 a65 a215 a77a29a216 a209a68a217a76a173 a172 a218 a77 a215 a219a27a220a159a221a183a222a85a223 a59a136a227a158a228 a152a130a150 a229 a65 (10) Here, a230a66a231a37a232a21a233a27a234a118a28a78a235a37a236a25a237a37a238a11a239a168a36 is the number of an N-gram and a230a66a231a37a232a21a233a27a234 a196 a197a29a240a98a241a243a242a244a28a78a235a37a236a25a237a37a238a49a239a168a36 denotes the number of ngram co-occurrences in a system output and the reference.
ROUGE-S ROUGE-S is an extension of ROUGE-2 defined as follows (Lin, 2004b): ROUGE-Sa59a61a146a31a62a98a147a49a65a68a67 a59a68a101a161a128a104a162 a2 a65a161a163 a157 a134a61a135a93a245a246 a2 a59a61a146a31a62a98a147a49a65a161a163 a145 a134a61a135a89a245a246 a2 a59a61a146a31a62a164a147a49a65 a157 a134a136a135a93a245a246 a2 a59a61a146a31a62a90a147a49a65a51a128a104a162 a2 a145 a134a61a135a89a245a246 a2 a59a61a146a31a62a98a147a49a65 (11) Where a166a168a169a78a170a248a247a250a249 a26 and a171a138a169a90a170a158a247a250a249 a26 are defined as follows: a251 a134a61a135a89a245a246 a2 a59a61a146a31a62a90a147a49a65a68a67 a252a248a253a85a254a255 a1 a59a61a146a31a62a90a147a49a65 # of skip bigram a2a23a147 (12) a3 a134a136a135a93a245a246 a2 a59a61a146a31a62a90a147a49a65a68a67 a252a83a253a118a254a255 a1 a59a61a146a31a62a90a147a49a65 # of skip bigram a2 a146 (13) Here, function Skip2 returns the number of skipbi-grams that are common to a141 and a139 . ROUGE-SU ROUGE-SU is an extension of ROUGE-S, which includes unigrams as a feature defined as follows (Lin, 2004b): ROUGE-SUa59a61a146a31a62a90a147a49a65a68a67 a59a68a101a161a128a49a162 a2 a65a117a163 a157 a134a5a4 a59a61a146a31a62a98a147a49a65a71a163 a145 a134a6a4 a59a61a146a31a62a98a147a49a65 a157 a134a5a4 a59a61a146a31a62a90a147a49a65a47a128a49a162 a2 a145 a134a5a4 a59a61a146a31a62a164a147a49a65 (14) Where a166 a169a8a7 and a171 a169a8a7 are defined as follows: a251 a134a5a4 a59a61a146a31a62a98a147a49a65a68a67 a252 a9 a59a61a146a31a62a90a147a49a65 (# of skip bigrams + # of unigrams) a2 a147 (15) a3 a134a5a4 a59a61a146a31a62a90a147a49a65a68a67 a252 a9 a59a61a146a31a62a90a147a49a65 (# of skip bigrams + # of unigrams) a2 a146 (16) Here, function SU returns the number of skip-bigrams and unigrams that are common to a141 and a139 . ROUGE-L ROUGE-L is an LCS-based evaluation measure defined as follows (Lin, 2004b): ROUGE-La59a61a146a31a62a90a147a49a65a68a67 a59a68a101a161a128a49a162 a2 a65a161a163 a157a11a10 a225a90a134 a59a61a146a31a62a90a147a49a65a161a163 a145a12a10 a225a90a134 a59a61a146a31a62a98a147a49a65 a157a11a10 a225a90a134 a59a61a146a31a62a90a147a49a65a47a128a49a162 a2 a145a12a10 a225a98a134 a59a61a146a31a62a90a147a49a65 (17) where a166a14a13a250a241a132a169 and a171a15a13a250a241a130a169 are defined as follows: a157a11a10 a225a98a134 a59a61a146a31a62a98a147a49a65a68a67 a101 a91 a16 a75 a77a29a216 LCSa17a244a59a61a156 a88 a62a90a146a21a65 (18) a145a18a10 a225a98a134 a59a61a146a31a62a98a147a49a65a68a67 a101 a95 a16 a75a78a77a83a216 LCSa17 a59a61a156a34a88a78a62a98a146a21a65 (19) Here, LCSa19a244a28a78a144a183a114a93a32a93a139a102a36 is the LCS score of the union longest common subsequence between reference sentences a144a25a114 and a139 . a115 and a122 are the number of words contained in a141, and a139, respectively.
a0 subsequence S1 S2 a0 subsequence S1 S2 a0 subsequence S1 S2 Becoming 1 1 Becoming-is a1 a2 a1 a2 astronaut-DREAM 0 a1 a2 DREAM 1 1 Becoming-my a1a4a3a5a1a4a3 astronaut-ambition 0 a1 a2 SPACEMAN 1 1 SPACEMAN-DREAM a1a4a3a5a1 a2 astronaut-is 0 1 a 1 0 SPACEMAN-ambition 0 a1 a2 astronaut-my 0 a1 ambition 0 1 SPACEMAN-dream a1 a3 0 cosmonaut-DREAM a1 a3 0 1 an 0 1 SPACEMAN-great a1 a2 0 cosmonaut-dream a1 a3 0 astronaut 0 1 SPACEMAN-is 1 1 cosmonaut-great a1 a2 0 cosmonaut 1 0 SPACEMAN-my a1a6a1 cosmonaut-is 1 0 dream 1 0 a-DREAM a1 a7 0 cosmonaut-my a1 0great 1 0 a-SPACEMAN 1 0 great-DREAM 1 0 is 1 1 2 a-cosmonaut 1 0 2 great-dream 1 0 my 1 1 a-dream a1 a7 0 is-DREAM a1 a2 a1 Becoming-DREAM a1a4a8a5a1 a7 a-great a1 a3 0 is-ambition 0 a1 Becoming-SPACEMAN a1a6a1 a-is a1 0 is-dream a1 a2 0 Becoming-a 1 0 a-my a1 a2 0 is-great a1 0 Becoming-ambition 0 a1 a7 an-DREAM 0 a1 a3 is-my 1 1 2 Becoming-an 0 1 an-SPACEMAN 0 1 my-DREAM a1 1 Becoming-astronaut 0 a1 an-ambition 0 a1 a3 my-ambition 0 1 Becoming-cosmonaut a1 0 an-astronaut 0 1 my-dream a1 0 Becoming-dream a1a4a8 0 an-is 0 a1 my-great 1 0 Becoming-great a1 a7 0 an-my 0 a1 a2 2002; Lin and Hovy, 2003; Lin, 2004a; Lin, 2004b; Soricut and Brill, 2004).
ACM Transactions on Computer-Human Interaction (TOCHI), 11(3).</rawString> </citation> <citation valid="true"> <authors> <author>M E Pollack</author> </authors> <title>Intelligent technology for an aging population: The use of AI to assist elders with cognitive impairment</title> <date>2005</date> <journal>AI Magazine</journal> <pages>26--2</pages> <contexts> <context>rch on developing SDS for home-care and tele-care applications, Examples include scheduling appointments over the phone (Zajicek et al. 2004, Wolters et al., submitted), interactive reminder systems (Pollack, 2005), symptom management systems (Black et al. 2005) or environmental control systems (Clarke et al. 2005).
Oxford, UK: Oxford University Press.</rawString> </citation> <citation valid="true"> <authors> <author>L A Black</author> <author>C McMeel</author> <author>M McTear</author> <author>N Black</author> <author>R Harper</author> <author>M Lemon</author> </authors> <title>Implementing autonomy in a diabetes management system</title> <date>2005</date> <journal>J Telemed Telecare</journal> <volume>11</volume> <contexts> <context>-care applications, Examples include scheduling appointments over the phone (Zajicek et al. 2004, Wolters et al., submitted), interactive reminder systems (Pollack, 2005), symptom management systems (Black et al. 2005) or environmental control systems (Clarke et al. 2005).
2Note that sentence extraction does not solve the problem of selecting and ordering summary sentences to form a coherent There are several approaches to modeling document content: simple word frequency-based methods (Luhn, 1958; Nenkova and Vanderwende, 2005), graph-based approaches (Radev, 2004; Wan and Yang, 2006), as well as more linguistically motivated techniques (Mckeown et al., 1999; Leskovec et al., 2005; Harabagiu et al., 2007).
2 Automatic Annotation Schemes Using ROUGE Similarity Measures ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is an automatic tool to determine the quality of a summary using a collection of measures ROUGE-N (N=1,2,3,4), ROUGE-L, ROUGE-W and ROUGE-S which count the number of overlapping units such as n-gram, word-sequences, and word-pairs between the extract and the abstract summaries (Lin, 2004).
Empirical evaluations using two standard summarization metricsthe Pyramid method (Nenkova and Passonneau, 2004b) and ROUGE (Lin, 2004)show that the best performing system is a CRF incorporating both order-2 Markov dependencies and skip-chain dependencies, which achieves 91.3% of human performance in Pyramid score, and outperforms our best-performing non-sequential model by 3.9%.
Considerations of sentence fluency are also key in sentence simplification (Siddharthan, 2003), sentence compression (Jing, 2000; Knight and Marcu, 2002; Clarke and Lapata, 2006; McDonald, 2006; Turner and Charniak, 2005; Galley and McKeown, 2007), text re-generation for summarization (Daume III and Marcu, 2004; Barzilay and McKeown, 2005; Wan et al., 2005) and headline generation (Banko et al., 2000; Zajic et al., 2007; Soricut and Marcu, 2007).
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 45 50 55 60 65 70 75 80 85Correlation Coefficient with Human Judgement (R) Human-Likeness Classifier Accuracy (%) Figure 1: This scatter plot compares classifiers accuracy with their corresponding metrics correlations with human assessments been previously observed by Liu and Gildea (2005).
But there is also extensive research focused on including linguistic knowledge in metrics (Owczarzak et al., 2006; Reeder et al., 2001; Liu and Gildea, 2005; Amigo et al., 2006; Mehay and Brew, 2007; Gimenez and M`arquez, 2007; Owczarzak et al., 2007; Popovic and Ney, 2007; Gimenez and M`arquez, 2008b) among others.
These dependencies differ from those used by Liu and Gildea (2005), in that they are extracted according to the rules of the LFG grammar and they are labelled with a type of grammatical relation that connects the head and the modifier, such as subject, determiner, etc. The presence of grammatical relation labels adds another layer of important linguistic information into the comparison and allows us to account for partial matches, for example when a lexical item finds itself in a correct relation but with an incorrect partner.
Our method, extending this line of research with the use of labelled LFG dependencies, partial matching, and n-best parses, allows us to considerably outperform Liu and Gildea?s (2005) highest correlations with human judgement (they report 0.144 for the correlation with human fluency judgement, 0.202 for the correlation with human overall judgement), although it has to be kept in mind that such comparison is only tentative, as their correlation is calculated on a different test set.
For instance, BLEU and ROUGE (Lin and Och, 2004) are based on n-gram precisions, METEOR (Banerjee and Lavie, 2005) and STM (Liu and Gildea, 2005) use word-class or structural information, Kauchak (2006) leverages on paraphrases, and TER (Snover et al., 2006) uses edit-distances.
Three kinds of metrics have been defined: 1http://www.lsi.upc.edu/nlp/IQMT 2http://svn.ask.it.usyd.edu.au/trac/ candc DR-STM-l (Semantic Tree Matching) These metrics are similar to the Syntactic Tree Matching metric defined by Liu and Gildea (2005), in this case applied to DRSs instead of constituency trees.
For instance, we may find metrics which compute similarities over shallow syntactic structures/sequences (Gimenez and M`arquez, 2007; Popovic and Ney, 2007), constituency trees (Liu and Gildea, 2005) and dependency trees (Liu and Gildea, 2005; Amigo et al., 2006; Mehay and Brew, 2007; Owczarzak et al., 2007).
1 Introduction B (Papineni et al., 2002) was one of the first automatic evaluation metrics for machine translation (MT), and despite being challenged by a number of alternative metrics (Melamed et al., 2003; Banerjee and Lavie, 2005; Snover et al., 2006; Chan and Ng, 2008), it remains the standard in the statistical MTliterature.Callison-Burchetal.(2006)havesubjected B to a searching criticism, with two realworld case studies of significant failures of correlation between B and human adequacy/fluency judgments.Bothcasesinvolvecomparisonsbetween statistical MT systems and other translation methods (human post-editing and a rule-based MT system), and they recommend that the use of B be restrictedtocomparisonsbetweenrelatedsystemsor different versions of the same systems.
In none of these cases did we repeat minimum-error-rate training; all these systems were trained using max-B. The metrics we tested were:  METEOR (Banerjee and Lavie, 2005), version 0.6,usingtheexact,Porter-stemmer,andWordNet synonmy stages, and the optimized parameters  = 0.81,  = 0.83,  = 0.28 as reported in (Lavie and Agarwal, 2007).
We have computed the BLEU score (accumulated up to 4-grams) (Papineni et al. , 2001), the NIST score (accumulated up to 5-grams) (Doddington, 2002), the General Text Matching (GTM) F-measure (e = 1,2) (Melamed et al. , 2003), and the METEOR measure (Banerjee and Lavie, 2005).
Others try to accommodate both syntactic and lexical differences between the candidate translation and the reference, like CDER (Leusch et al. , 2006), which employs a version of edit distance for word substitution and reordering; METEOR (Banerjee and Lavie, 2005), which uses stemming and WordNet synonymy; and a linear regression model developed by (Russo-Lassner et al. , 2005), which makes use of stemming, WordNet synonymy, verb class synonymy, matching noun phrase heads, and proper name matching.
For evaluation we have selected a set of 8 metric variants corresponding to seven different families: BLEU (n = 4) (Papineni et al. , 2001), NIST (n = 5) (Lin and Hovy, 2002), GTM F1-measure (e = 1,2) (Melamed et al. , 2003), 1-WER (Nieen et al. , 2000), 1-PER (Leusch et al. , 2003), ROUGE (ROUGE-S*) (Lin and Och, 2004) and METEOR3 (Banerjee and Lavie, 2005).
Comparing the LFG-based evaluation method with other popular metrics: BLEU, NIST, General Text Matcher (GTM) (Turian et al. , 2003), Translation Error Rate (TER) (Snover et al. , 2006)1, and METEOR (Banerjee and Lavie, 2005), we show that combining dependency representations with paraphrases leads to a more accurate evaluation that correlates better with human judgment.
In an experiment on 16,800 sentences of Chinese-English newswire text with segment-level human evaluation from the Linguistic Data Consortium?s (LDC) Multiple Translation project, we compare the LFG-based evaluation method with other popular metrics like BLEU, NIST, General Text Matcher (GTM) (Turian et al. , 2003), Translation Error Rate (TER) (Snover et al. , 2006)1, and METEOR (Banerjee and Lavie, 2005), and we show that combining dependency representations with synonyms leads to a more accurate evaluation that correlates better with human judgment.
They are: ??Meteor (Banerjee and Lavie, 2005)?Meteor measures precision and recall of unigrams when comparing a hypothesis translation 142 Language Pair Test Set Adequacy Fluency Rank Constituent English-German Europarl 1,416 1,418 1,419 2,626 News Commentary 1,412 1,413 1,412 2,755 German-English Europarl 1,525 1,521 1,514 2,999 News Commentary 1,626 1,620 1,601 3,084 English-Spanish Europarl 1,000 1,003 1,064 1,001 News Commentary 1,272 1,272 1,238 1,595 Spanish-English Europarl 1,174 1,175 1,224 1,898 News Commentary 947 949 922 1,339 English-French Europarl 773 772 769 1,456 News Commentary 729 735 728 1,313 French-English Europarl 834 833 830 1,641 News Commentary 1,041 1,045 1,035 2,036 English-Czech News Commentary 2,303 2,304 2,331 3,968 Czech-English News Commentary 1,711 1,711 1,733 0 Totals 17,763 17,771 17,820 27,711 Table 2: The number of items that were judged for each task during the manual evaluation against a reference.
While these are based on a relatively few number of items, and while we have not performed any tests to determine whether the differences in ? are statistically significant, the results 7The Czech-English conditions were excluded since there were so few systems 146 are nevertheless interesting, since three metrics have higher correlation than Bleu: ??Semantic role overlap (Gimenez and M`arquez, 2007), which makes its debut in the proceedings of this workshop ??ParaEval measuring recall (Zhou et al. , 2006), which has a model of allowable variation in translation that uses automatically generated paraphrases (Callison-Burch, 2007) ??Meteor (Banerjee and Lavie, 2005) which also allows variation by introducing synonyms and by flexibly matches words using stemming.
Table 2: Corpora and Modalities CORPUS MODALITY ACE asserted, or other TIMEML must, may, should, would, or could Prasad et al., 2006 assertion, belief, facts or eventualities Saur et al., 2007 certain, probable, possible, or other Inui et al., 2008 affirm, infer, doubt, hear, intend, ask, recommend, hypothesize, or other THIS STUDY S/O, necessity, hope, possible, recommend, intend   Table 3: Markup Scheme (Tags and Definitions) Tag Definition (Examples) R Remedy, Medical operation (e.g. radiotherapy) T Medical test, Medical examination (e.g., CT, MRI) D Deasese, Symptom (e.g., Endometrial cancer, headache) M Medication, administration of a drug (e.g., Levofloxacin, Flexeril) A patient action (e.g., admitted to a hospital) V Other verb (e.g., cancer spread to )   2 Related Works 2.1 Previous Markup Schemes In the NLP field, fact identification has not been studied well to date.
c2007 Association for Computational Linguistics Structural Correspondence Learning for Dependency Parsing Nobuyuki Shimizu Information Technology Center University of Tokyo Tokyo, Japan shimizu@r.dl.itc.u-tokyo.ac.jp Hiroshi Nakagawa Information Technology Center University of Tokyo Tokyo, Japan nakagawa@dl.itc.u-tokyo.ac.jp Abstract Following (Blitzer et al. , 2006), we present an application of structural correspondence learning to non-projective dependency parsing (McDonald et al. , 2005).
For each pivot feature k, we use a loss function L k , () 2 1)( wxwxpL i i T ikk +=         (1) where the function p k (x i ) indicates whether the pivot feature k occurs in the instance x i , otherwise xif xp ik ik 0 1 1 )( >     = , where the weight vector w encodes the correspondence of the non-pivot features with the pivot feature k (Blitzer et al., 2006).
Much of the work in subjectivity analysis has been applied to English data, though work on other languages is growing: e.g., Japanese data are used in (Kobayashi et al., 2004; Suzuki et al., 2006; Takamura et al., 2006; Kanayama and Nasukawa, 2006), Chinese data are used in (Hu et al., 2005), and German data are used in (Kim and Hovy, 2006).
Automatic approaches to creating a semantic orientation lexicon and, more generally, approaches for word-level sentiment annotation can be grouped into two kinds: (1) those that rely on manually created lexical resourcesmost of which use WordNet (Strapparava and Valitutti, 2004; Hu and Liu, 2004; Kamps et al., 2004; Takamura et al., 2005; Esuli and Sebastiani, 2006; An1http://www.wjh.harvard.edu/ inquirer 599 dreevskaia and Bergler, 2006; Kanayama and Nasukawa, 2006); and (2) those that rely on text corpora (Hatzivassiloglou and McKeown, 1997; Turney and Littman, 2003; Yu and Hatzivassiloglou, 2003; Grefenstette et al., 2004).
A variety of approaches have been investigated for speech summarization, for example, maximum entropy, conditional random fields, latent semantic analysis, support vector machines, maximum marginal relevance (Maskey and Hirschberg, 2003; Hori et al., 2003; Buist et al., 2005; Galley, 2006; Murray et al., 2005; Zhang et al., 2007; Xie and Liu, 2008).
2 Related Work To model the syntactic transformation process, researchers in these fieldsespecially in machine translationhave developed powerful grammatical formalisms and statistical models for representing and learning these tree-to-tree relations (Wu and Wong, 1998; Eisner, 2003; Gildea, 2003; Melamed, 2004; Ding and Palmer, 2005; Quirk et al. , 2005; Galley et al. , 2006; Smith and Eisner, 2006, inter alia).
Reranking approaches (Charniak and Johnson, 2005; Chen et al. , 2002; Collins and Koo, 2005; Ji et al. , 2006; Roark et al. , 2006) have been successfully applied to many NLP applications, including parsing, named entity recognition, sentence boundary detection, etc. To the best of our knowledge, reranking approaches have not been used for POS tagging, possibly due to the already high levels of accuracy for English, which leave little room for further improvement.
(1) a. Please move your car Her sadness moves him b. John enjoys the book John enjoys reading the book e. The two alibis do not accord They accorded him a warm welcome d. John swam for hours John swam across the channel Although the precise nrechanisms which govern lexical knowledge are still largely unknown, there is strong evidence that word sense extensibi\[ity is not arbitrary (Atkins &: Levin, 1991; Pustejovsky, 1991, 1994; Ostler Atkius, 1991).