gaof23/citation_sentiment_corpus huggingface dataset:
(1) a. I expected \[nv the man who smoked NP\] to eat ice-cream h. I doubted \[NP the man who liked to eat ice-cream NP\] Current high-coverage parsers tend to use either custom, hand-generated lists of subcategorization frames (e.g. , Hindle, 1983), or published, handgenerated lists like the Ozford Advanced Learner's Dictionary of Contemporary English, Hornby and Covey (1973) (e.g. , DeMarcken, 1990).
When the value of Ilw, r,w'll is unknown, we assume that A and C are conditionally independent given B. The probability of A, B and C cooccurring is estimated by PMLE( B ) PMLE( A\[B ) PMLE( C\[B ), where PMLE is the maximum likelihood estimation of a probability distribution and P.LE(B) = II*,*,*ll' P. ,~E(AIB ) = II*,~,*ll ' P, LE(CIB) = When the value of Hw, r, w~H is known, we can obtain PMLE(A, B, C) directly: PMLE(A, B, C) = \[\[w, r, wll/\[\[*, *, *H Let I(w,r,w ~) denote the amount information contained in Hw, r,w~\]\]=c. Its value can be corn769 simgindZe(Wl, W2) = ~'~(r,w)eTCwl)NTCw2)Aresubj.of.obj-of} min(I(Wl, r, w), I(w2, r, w) ) simHindte, (Wl, W2) = ~,(r,w)eT(w,)nT(w2) min(I(wl, r, w), I(w2, r, w)) \]T(Wl)NT(w2)I simcosine(Wl,W2) = x/IZ(w~)llZ(w2)l 2x IT(wl)nZ(w2)l simDice(Wl, W2) = iT(wl)l+lT(w2) I simJacard (Wl, W2) = T(wl )OT(w2)l T(wl) + T(w2)l-IT(Wl)rlT(w2)l Figure 1: Other Similarity Measures puted as follows: I(w,r,w') = _ Iog(PMLE(B)PMLE(A\]B)PMLE(CIB)) --(-log PMLE(A, B, C)) log IIw,r,wflll*,r,*ll -IIw,r,*ll xll*,r,w'll It is worth noting that I(w,r,w') is equal to the mutual information between w and w' (Hindle, 1990).
For example, the words corruption and abuse are similar because both of them can be subjects of verbs like arouse, become, betray, cause, continue, cost, exist, force, go on, grow, have, increase, lead to, and persist, etc, and both of them can modify nouns like accusation, act, allegation, appearance, and case, etc. Many methods have been proposed to compute distributional similarity between words, e.g., (Hindle, 1990), (Pereira et al. 1993), (Grefenstette 1994) and (Lin 1998).
There are many different similarity measures, which variously use taxonomic lexical hierarchies or lexical-semantic networks, large text corpora, word definitions in machine-readable dictionaries or other semantic formalisms, or a combination of these (Dagan, Marcus, and Markovitch 1993; Kozima and Furugori 1993; Pereira, Tishby, and Lee 1993; Church et al. 1994; Grefenstette 1994; Resnik 1995; McMahon and Smith 1996; Jiang and Conrath 1997; Sch utze 1998; Lin 1998; Resnik and Diab 2000; Budanitsky 1999; Budanitsky and Hirst 2001, 2002).
This can be done by smoothing the observed frequencies 7 (Church and Mercer 1993) or by class-based methods (Brown et al. 1991; Pereira and Tishby 1992; Pereira, Tishby, and Lee 1993; Hirschman 1986; Resnik 1992; Brill et al. 1990; Dagan, Marcus, and Markovitch 1993).
A Broad-Coverage Word Sense Tagger Dekang Lin Department of Computer Science University of Manitoba Winnipeg, Manitoba, Canada R3T 2N2 lindek@cs.umanitoba.ca Previous corpus-based Word Sense Disambiguation (WSD) algorithms (Hearst, 1991; Bruce and Wiebe, 1994; Leacock et al. , 1996; Ng and Lee, 1996; Yarowsky, 1992; Yarowsky, 1995) determine the meanings of polysemous words by exploiting their local contexts.
However, the best performing statistical approaches to lexical ambiguity resolution l;lmmselves rely on complex infornmtion sources such as "lemmas, inflected forms, parts of speech and arbitrary word classes If\] local and distant collocations, trigram sequences, a.nd predicate m'gument association" (Yarowsky (1995), p. 190) or large context-windows up to 1000 neighboring words (Sch/itze, 1992).
An alternative method we considered was to estimate certain conditional probabilities, similarly to the formula used in (Yarowsky, 1995): SW(t) log P(p C A/t) f(t, A)f(A) = ~ log (2) P(p C R/t) f(t, .R)f(.l~) Here f(A) is (an estimate of) the probability that any given candidate phrase will be accepted by the spotter, and f(R) is the probability that this phrase is rejected, i.e., f(R) = l-f (A).
213 Proceedings of EACL '99 Table 2: The result of disambiguation experiment(two senses) (6) \[__ 122 "-~cause~ e~'ect ~ require a-~ "-Telose, open, ~ rrect(~ "-'(fall, decline, win} \] 278 "-~feel, think, sense T T 280 {hit, attack, strike} I 250 {leave, remain, go} \[ 183 gcty t ~Ol accomplish, operate'}-216 --{occur, happen, ~ --{order, request, arrange-'~"~ 240 "-~ass, adopt, ~ 274 -'~roduce, create, gro'~~"--""2~ --~ush, attack, pull~ -~s~ve, 223 "-{ship, put, send} {stop, end, move} {add, append, total} {keep, maintain, protect} Total 215(77.3 181(72.4 160(87.4 349(92.3) ~-~ Correct(%)\] 83(77.0) 113(86.2) I 169(87.5) J Yarowsky used an unsupervised learning procedure to perform noun WSD (Yarowsky, 1995).
1 Introduction One of the major approaches to disambiguate word senses is supervised learning (Gale et al. , 1992), (Yarowsky, 1992), (Bruce and Janyce, 1994), (Miller et al. , 1994), (Niwa and Nitta, 1994), (Luk, 1995), (Ng and Lee, 1996), (Wilks and Stevenson, 1998).
Many corpus based statistical methods have been proposed to solve this problem, including supervised learning algorithms (Leacock et al. , 1998; Towel and Voorheest, 1998), weakly supervised learning algorithms (Dagan and Itai, 1994; Li and Li, 2004; Mihalcea, 2004; Niu et al. , 2005; Park et al. , 2000; Yarowsky, 1995), unsupervised learning algorithms (or word sense discrimination) (Pedersen and Bruce, 1997; Schutze, 1998), and knowledge based algorithms (Lesk, 1986; McCarthy et al. , 2004).
A variety of classifiers have been employed for this task (see Mooney [1996] and Ide and Veronis [1998] for overviews), the most popular being decision lists (Yarowsky 1994, 1995) and naive Bayesian classifiers (Pedersen 2000; Ng 1997; Pedersen and Bruce 1998; Mooney 1996; Cucerzan and Yarowsky 2002).
(1992), Pereira and Tishby (1992), and Pereira, Tishby, and Lee (1993) propose methods that derive classes from the distributional properties of the corpus itself, while other authors use external information sources to define classes: Resnik (1992) uses the taxonomy of WordNet; Yarowsky (1992) uses the categories of Roget's Thesaurus, Slator (1992) and Liddy and Paik (1993) use the subject codes in the LDOCE; Luk (1995) uses conceptual sets built from the LDOCE definitions.
Word Senses Sample Size Feedback Size % Correct % Correct per Sense Total drug narcotic 65 100 92.3 90.5 medicine 83 65 89.1 sentence judgment 23 327 100.0 92.5 grammar 4 42 50.0 suit court 212 1,461 98.6 94.8 garment 21 81 55.0 player performer 48 230 87.5 92.3 participant 44 1,552 97.7 the feedback sets) consisted of a few dozen examples, in comparison to thousands of examples needed in other corpus-based methods (Sch~itze 1992; Yarowsky 1995).
(~ 1998 Association for Computational Linguistics Computational Linguistics Volume 24, Number 1 1995), (3) thesaurus categories (Yarowsky 1992; Chen and Chang 1994), (4) translation in another language (Gale, Church, and Yarowsky 1992; Dagan, Itai, and Schwall 1991; Dagan and Itai 1994), (5) automatically induced clusters with sublexical representation (Schiitze 1992), and (6) hand-crafted lexicons (McRoy 1992).
Various corpus-based approaches to word sense disambiguation have been proposed (Bruce and Wiebe 1994; Charniak 1993; Dagan and Itai 1994; Fujii et al. 1996; Hearst 1991; Karov and Edelman 1996; Kurohashi and Nagao 1994; Li, Szpakowicz, and Matwin 1995; Ng and Lee 1996; Niwa and Nitta 1994; Sch~itze 1992; Uramoto 1994b; Yarowsky 1995).
This includes the automatic generation of sense-tagged data using monosemous relatives (Leacock et al. , 1998; Mihalcea and Moldovan, 1999; Agirre and Martinez, 2004), automatically bootstrapped disambiguation patterns (Yarowsky, 1995; Mihalcea, 2002), parallel texts as a way to point out word senses bearing different translations in a second language (Diab and Resnik, 2002; Ng et al. , 2003; Diab, 2004), and the use of volunteer contributions over the Web (Chklovski and Mihalcea, 2002).
Numerous approaches have been explored for exploiting situations where some amount of annotated data is available and a much larger amount of data exists unannotated, e.g. Marialdo's HMM part-of-speech tagger training (1994), Charniak's parser retraining experiment (1996), Yarowsky's seeds for word sense disambiguation (1995) and Nigam et al's (1998) topic classifier learned in part from unlabelled documents.
The more recent set of techniques includes mult iplicative weightupdate algorithms (Golding and Roth, 1998), latent semantic analysis (Jones and Martin, 1997), transformation-based learning (Mangu and Brill, 1997), differential grammars (Powers, 1997), decision lists (Yarowsky, 1994), and a variety of Bayesian classifiers (Gale et al. , 1993, Golding, 1995, Golding and Schabes, 1996).
All features encountered in the training data are ranked in the DL (best evidence first) according to the following loglikelihood ratio (Yarowsky, 1995): Log Pr(reading i jfeature k ) P j6=i Pr(reading j jfeature k ) We estimated probabilities via maximum likelihood, adopting a simple smoothing method (Martinez and Agirre, 2000): 0.1 is added to both the denominator and numerator.
Supervised approaches which make use of a small hand-labeled training set (Bruce and Wiebe, 1994; Yarowsky, 1993) typically outperform unsupervised approaches (Agirre et al. , 2000; Litkowski, 2000; Lin, 2000; Resnik, 1997; Yarowsky, 1992; Yarowsky, 1995), but tend to be tuned to a speci c corpus and are constrained by scarcity of labeled data.
They roughly fall into three categories according to what is used for supervision in learning process: (1) using external resources, e.g., thesaurus or lexicons, to disambiguate word senses or automatically generate sense-tagged corpus, (Lesk, 1986; Lin, 1997; McCarthy et al. , 2004; Seo et al. , 2004; Yarowsky, 1992), (2) exploiting the differences between mapping of words to senses in different languages by the use of bilingual corpora (e.g. parallel corpora or untagged monolingual corpora in two languages) (Brown et al. , 1991; Dagan and Itai, 1994; Diab and Resnik, 2002; Li and Li, 2004; Ng et al. , 2003), (3) bootstrapping sensetagged seed examples to overcome the bottleneck of acquisition of large sense-tagged data (Hearst, 1991; Karov and Edelman, 1998; Mihalcea, 2004; Park et al. , 2000; Yarowsky, 1995).
To compare the performance of different taggers learned by different mechanisms, one can measure the precision, recall and F-measure, given by precision = # correct predictions# predicted gene mentions recall = # correct predictions# true gene mentions F-measure = a96a15a14 precision a14 recallprecision a44 recall In our evaluation, we compared the proposed semi-supervised learning approach to the state of the art supervised CRF of McDonald and Pereira (2005), and also to self-training (Celeux and Govaert 1992; Yarowsky 1995), using the same feature set as (McDonald and Pereira 2005).
Many approaches have been proposed for semisupervised learning in the past, including: generative models (Castelli and Cover 1996; Cohen and Cozman 2006; Nigam et al. 2000), self-learning (Celeux and Govaert 1992; Yarowsky 1995), cotraining (Blum and Mitchell 1998), informationtheoretic regularization (Corduneanu and Jaakkola 2006; Grandvalet and Bengio 2004), and graphbased transductive methods (Zhou et al. 2004; Zhou et al. 2005; Zhu et al. 2003).
Consequently, semi-supervised learning, which combines both labeled and unlabeled data, has been applied to some NLP tasks such as word sense disambiguation (Yarowsky, 1995; Pham et al. , 2005), classification (Blum and Mitchell, 1998; Thorsten, 1999), clustering (Basu et al. , 2004), named entity classification (Collins and Singer, 1999), and parsing (Sarkar, 2001).
2 Related Work WSD approaches can be classified as (a) knowledge-based approaches, which make use of linguistic knowledge, manually coded or extracted from lexical resources (Agirre and Rigau, 1996; Lesk 1986); (b) corpus-based approaches, which make use of shallow knowledge automatically acquired from corpus and statistical or machine learning algorithms to induce disambiguation models (Yarowsky, 1995; Schtze 1998); and (c) hybrid approaches, which mix characteristics from the two other approaches to automatically acquire disambiguation models from corpus supported by linguistic knowledge (Ng and Lee 1996; Stevenson and Wilks, 2001).
c2008 Association for Computational Linguistics Refining Event Extraction through Cross-document Inference   Heng Ji Ralph Grishman Computer Science Department New York University New York, NY 10003, USA (hengji, grishman)@cs.nyu.edu       Abstract We apply the hypothesis of One Sense Per Discourse (Yarowsky, 1995) to information extraction (IE), and extend the scope of discourse from one single document to a cluster of topically-related documents.
1 Introduction Co-training (Blum and Mitchell, 1998), and several variants of co-training, have been applied to a number of NLP problems, including word sense disambiguation (Yarowsky, 1995), named entity recognition (Collins and Singer, 1999), noun phrase bracketing (Pierce and Cardie, 2001) and statistical parsing (Sarkar, 2001; Steedman et al. , 2003).
Similarlyto(Collins and Singer, 1999; Yarowsky, 1995), we define the strength of a pattern p in a category y as the precision of p in the set of documents labeled with category y, estimated using Laplace smoothing: strength(p,y) = count(p,y) + epsilon1count(p) + kepsilon1 (3) where count(p,y) is the number of documents labeled y containing pattern p, count(p) is the overall number of labeled documents containing p, and k is the number of domains.
1http://www.nist.gov/speech/tests/ace/ 49 Bootstrapping techniques have been used for such diverse NLP problems as: word sense disambiguation (Yarowsky, 1995), named entity classification (Collins and Singer, 1999), IE pattern acquisition (Riloff, 1996; Yangarber et al., 2000; Yangarber, 2003; Stevenson and Greenwood, 2005), document classification (Surdeanu et al., 2006), fact extraction from the web (Pasca et al., 2006) and hyponymy relation extraction (Kozareva et al., 2008).
Attempts to alleviate this tagbottleneck i~lude tmotstr~ias (Te~ ot ill,, 1996; Hearst, 1991) and unsupervised algorith~ (Yarowsky, 199s) Dictionary-based approaches rely on linguistic knowledge sources such as ma~l~i,~e-readable dictionaries (Luk, 1995; Veronis and Ide, 1990) and WordNet (Agirre and Rigau, 1996; Resnik, 1995) and e0(ploit these for word sense disaznbiguation.
Introduction Word sense disambiguation has long been one of the major concerns in natural language processing area (e.g. , Bruce et al. , 1994; Choueka et al. , 1985; Gale et al. , 1993; McRoy, 1992; Yarowsky 1992, 1994, 1995), whose aim is to identify the correct sense of a word in a particular context, among all of its senses defined in a dictionary or a thesaurus.
1 Introduction Word compositions have long been a concern in lexicography(Benson et al. 1986; Miller et al. 1995), and now as a specific kind of lexical knowledge, it has been shown that they have an important role in many areas in natural language processing, e.g., parsing, generation, lexicon building, word sense disambiguation, and information retrieving, etc.(e.g. , Abney 1989, 1990; Benson et al. 1986; Yarowsky 1995; Church and Hanks 1989; Church, Gale, Hans, and Hindle 1989).
234 ADV Non-specific adverbial BNF Benefemtive CLF It-cleft CLR 'Closely related' DIR Direction DTV Dative EXT Extent HLN Headline LGS Logical subject L0C Location MNI~ Manner N0M Nominal PRD Predicate PRP Purpose PUT Locative complement of 'put' SBJ Subject TMP Temporal TPC Topic TTL Title V0C Vocative Grammatical DTV 0.48% LGS 3.0% PRD 18.% PUT 0.26% SBJ 78.% v0c 0.025% Figure 1: Penn treebank function tags 53.% Form/Function 37.% Topicalisation 2.2% 0.25% NOM 6.8% 2.5% TPC 100% 2.2% 1.5% ADV 11.% 4.2% 9.3% BN'F 0.072% 0.026% 0.13% DIR 8.3% 3.0% 41.% EXT 3.2% 1.2% 0.013% LOC 25.% 9.2% MNR 6.2% 2.3% PI~ 5.2% 1.9% 33.% 12.% Miscellaneous 9.5% CLR 94.% 8.8% CLF 0.34% 0.03% HLN 2.6% 0.25% TTL 3.1% 0.29% Figure 2: Categories of function tags and their relative frequencies one project that used them at all: (Collins, 1997) defines certain constituents as complements based on a combination of label and function tag information.
:~ The difl'erent kinds of noun chunks covered by our grmnmar are listed below and illustrated with exmnples:  a combination of a non-obligatory deternfiner, optional adjectives or cardinals and the noun 1Other types of lexicalised PCFGs have been (h!scrib('.d in (Charniak, 1997), (Collins, 1997), (G'oodman, 1997), (Chcll)a and .lelinek, 1998) mid (Eisner and Sat:a, 1999).
There have been a lot of prol)OS~fls for statistical analysis, in ninny languages, in particular in English and Japanese (Magerman, 1995) (Sekine and Grishman, 1995) (Collins, 1997) (I/atnal)arkhi, 1997) (K.Shirai et.al, 1998) (Fujio and Matsnlnoto, 1998) (Itaruno ct.al, 1997)(Ehara, 1998).
Table 2 shows the dependency accuracy, root accuracy and complete match scores for our best parser (Model 2 with label set B) in comparison with Collins (1997) (Model 3), Charniak (2000), and Yamada and Matsumoto (2003).5 It is clear that, with respect to unlabeled accuracy, our parser does not quite reach state-of-the-art performance, even if we limit the competition to deterministic methods such as that of Yamada and Matsumoto (2003).
All state-of-the-art wide-coverage parsers relax this assumption in some way, for instance by (i) changing the parser in step (3), such that the application of rules is conditioned on other steps in the derivation process (Collins, 1997; Charniak, 1997), or by (ii) enriching the nonterminal labels in step (1) with context-information (Johnson, 1998; Klein and Manning, 2003), along with suitable backtransforms in step (4).
While it was initially believed that lexicalization of PCFG parsers (Collins, 1997; Charniak, 2000) is crucial for obtaining good parsing results, Gildea (2001) demonstrated that the lexicalized Model-1 parser of Collins (1997) does not benefit from bilexical information when tested on a new text domain, and only marginally benefits from such information when tested on the same text domain as the training corpora.
((:I (:Q DET NAMED-ENTITY) ENTER[V] (:Q THE ROOM[N])) (:I (:Q DET FEMALE-INDIVIDUAL) HAVE[V] (:Q DET ROOM[N])) (:I (:Q DET FEMALE-INDIVIDUAL) SLEEP[V]) (:I (:Q DET FEMALE-INDIVIDUAL) HAVE[V] (:Q DET (:F PLUR CLOTHE[N]))) (:I (:Q DET (:F PLUR CLOTHE[N])) WASHED[A])) Here the upper-case sentences are automatically generated verbalizations of the abstracted LFs shown beneath them.1 The initial development of KNEXT was based on the hand-constructed parse trees in the Penn Treebank version of the Brown corpus, but subsequently Schubert and collaborators refined and extended the system to work with parse trees obtained with statistical parsers (e.g., that of Collins (1997) or Charniak (2000)) applied to larger corpora, such as the British National Corpus (BNC), a 100 million-word, mixed genre collection, along with Web corpora of comparable size (see work of Van Durme et al.
Perhaps the most widely accepted convention is that of ignoring punctuation for the purposes of assigning constituent span, under the perspective that, fun788 Phrase Evaluation Scenario System Type (a) (b) (c) Modified All 98.37 99.72 99.72 Truth VP 92.14 98.70 98.70 Li and Roth All 94.64 (2001) VP 95.28 Collins (1997) All 92.16 93.42 94.28 VP 88.15 94.31 94.42 Charniak All 93.88 95.15 95.32 (2000) VP 88.92 95.11 95.19 Table 1: F-measure shallow bracketing accuracy under three different evaluation scenarios: (a) baseline, used in Li and Roth (2001), with original chunklink script converting treebank trees and context-free parser output; (b) same as (a), except that empty subject NPs are inserted into every unary SVP production; and (c) same as (b), except that punctuation is ignored for setting constituent span.
The differences between a k-best and a beam-search parser (not to mention the use of dynamic programming) make a running time difference unsur17 Our score of 85.8 average labeled precision and recall for sentences less than or equal to 100 on Section 23 compares to: 86.7 in Charniak (1997), 86.9 in Ratnaparkhi (1997), 88.2 in Collins (1999), 89.6 in Charniak (2000), and 89.75 in Collins (2000).
Moreover, in order to determine whether the performances of the predictive criteria are consistent across different learning models within the same domain, we have performed the study on two parsing models: one based on a context-free variant of tree-adjoining grammars (Joshi, Levy, and Takahashi 1975), the Probabilistic Lexicalized Tree Insertion Grammar (PLTIG) formalism (Schabes and Waters 1993; Hwa 1998), and Collinss Model 2 parser (1997).
In particular, previous work (Ratnaparkhi, Roukos, and Ward 1994; Abney 1997; Della Pietra, Della Pietra, and Lafferty 1997; Johnson et al. 1999; Riezler et al. 2002) has investigated the use of Markov random fields (MRFs) or log-linear models as probabilistic models with global features for parsing and other NLP tasks.
Manually defined heuristics are used to automatically annotate each tree in the treebank with partially specified HPSG derivation trees: Head/argument/modifier distinctions are made for each node in the tree based on Magerman (1994) and Collins (1997); 336 ODonovan et al. Large-Scale Induction and Evaluation of Lexical Resources the whole tree is then converted to a binary tree; heuristics are applied to deal with phenomena such as LDDs and coordination and to correct some errors in the treebank, and finally an HPSG category is assigned to each node in the tree in accordance with its CFG category.
Recently, specic probabilistic tree-based models have been proposed not only for machine translation (Wu 1997; Alshawi, Bangalore, and Douglas 2000; Yamada and Knight 2001; Eisner 2003; Gildea 2003), but also for summarization (Knight and Marcu 2002), paraphrasing (Pang, Knight, and Marcu 2003), natural language generation (Langkilde and Knight 1998; Bangalore and Rambow 2000; Corston-Oliver et al. 2002), parsing, and language modeling (Baker 1979; Lari and Young 1990; Collins 1997; Chelba and Jelinek 2000; Charniak 2001; Klein  Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA 90292.
Model interpolation in this case perSystem Training Heldout LR LP MAP Brown;T Brown;H 76.0 75.4 MAP Brown;T WSJ;24 76.9 77.1 Gildea WSJ;2-21 86.1 86.6 MAP WSJ;2-21 WSJ;24 86.9 87.1 Charniak (1997) WSJ;2-21 WSJ;24 86.7 86.6 Ratnaparkhi (1999) WSJ;2-21 86.3 87.5 Collins (1999) WSJ;2-21 88.1 88.3 Charniak (2000) WSJ;2-21 WSJ;24 89.6 89.5 Collins (2000) WSJ;2-21 89.6 89.9 Table 4: Parser performance on WSJ;23, baselines.
summarization (Knight and Marcu, 2002), paraphrasing (Pang, Knight, and Marcu, 2003), natural language generation (Langkilde and Knight, 1998; Bangalore and Rambow, 2000; Corston-Oliver et al. , 2002), and language modeling (Baker, 1979; Lari and Young, 1990; Collins, 1997; Chelba and Jelinek, 2000; Charniak, 2001; Klein and Manning, 2003).
Probability estimates of the RHS given the LHS are often smoothed by making a Markov assumption regarding the conditional independence of a category on those more than k categories away (Collins, 1997; Charniak, 2000): P(X  Y1Yn)= P(Y1|X) nY i=2 P(Yi|X,Y1 Yi1)  P(Y1|X) nY i=2 P(Yi|X,Yik Yi1).
Many probabilistic evaluation models have been published inspired by one or more of these feature types [Black, 1992] [Briscoe, 1993] [Charniak, 1997] [Collins, 1996] [Collins, 1997] [Magerman, 1995] [Eisner, 1996], but discrepancies between training sets, algorithms, and hardware environments make it difficult, if not impossible, to compare the models objectively.
1 Introduction In the field of statistical parsing, various probabilistic evaluation models have been proposed where different models use different feature types [Black, 1992] [Briscoe, 1993] [Brown, 1991] [Charniak, 1997] [Collins, 1996] [Collins, 1997] [Magerman, 1991] [Magerman, 1992] [Magerman, 1995] [Eisner, 1996].