gaof23/citation_sentiment_corpus huggingface dataset:
Endemic structural ambiguity, which can lead to such difficulties as trying to cope with the many thousands of possible parses that a grammar can assign to a sentence, can be greatly reduced by adding empirically derived probabilities to grammar rules (Fujisaki et al. 1989; Sharman, Jelinek, and Mercer 1990; Black et al. 1993) and by computing statistical measures of lexical association (Hindle and Rooth 1993).
Some of these methods make use of prior knowledge in the form of an existing thesaurus (Resnik 1993a, 1993b; Framis 1994; Almuallim et al. 1994; Tanaka 1996; Utsuro and Matsumoto 1997), while others do not rely on any prior knowledge (Pereira, Tishby, and Lee 1993; Grishman and Sterling 1994; Tanaka 1994).
http://duc.nist.gov</title> <date>2004</date> <journal>Journal of the Association for Computing Machinery</journal> <volume>16</volume> <pages>264--285</pages> <contexts> <context> (Voorhees and Harman, 1999), Message Understanding Conferences (MUC) (Chinchor et al, 1993), TIPSTER SUMMAC Text Summarization Evaluation (Mani et al, 1998), Document Understanding Conference (DUC) (DUC, 2004), and Text Summarization Challenge (TSC) (Fukushima and Okumura, 2001), have attested the importance of this topic.
264-285.</rawString> </citation> <citation valid="true"> <authors> <author>T Fukushima</author> <author>M Okumura</author> </authors> <title>Text summarization challenge: text summarization in Japan</title> <date>2001</date> <booktitle>in Proceedings of NAACL 2001 Workshop Automatic Summarization</booktitle> <pages>51--59</pages> <contexts> <context>Conferences (MUC) (Chinchor et al, 1993), TIPSTER SUMMAC Text Summarization Evaluation (Mani et al, 1998), Document Understanding Conference (DUC) (DUC, 2004), and Text Summarization Challenge (TSC) (Fukushima and Okumura, 2001), have attested the importance of this topic.
Statistics in linguistics, Oxford.: Basil Blackwell.</rawString> </citation> <citation valid="true"> <authors> <author>N Chinchor</author> </authors> <title>Evaluating message understanding systems: an analysis of the third Message Understanding Conference (MUC-3</title> <date>1993</date> <journal>Computational Linguistics</journal> <volume>19</volume> <pages>409--449</pages> <marker>Chinchor, 1993</marker> <rawString>Chinchor, N., et al, 1993.
In acknowledgment of this fact, a series of conferences like Text Retrieval Conferences (TREC) (Voorhees and Harman, 1999), Message Understanding Conferences (MUC) (Chinchor et al, 1993), TIPSTER SUMMAC Text Summarization Evaluation (Mani et al, 1998), Document Understanding Conference (DUC) (DUC, 2004), and Text Summar</context> </contexts> <marker>Voorhees, Harman, 1999</marker> <rawString>Voorhees, E. M. and Harman, D. K., 1999.
3 Building the CatVar The CatVar database was developed using a combination of resources and algorithms including the Lexical Conceptual Structure (LCS) Verb and Preposition Databases (Dorr, 2001), the Brown Corpus section of the Penn Treebank (Marcus et al. , 1993), an English morphological analysis lexicon developed for PC-Kimmo (Englex) (Antworth, 1990), NOMLEX (Macleod et al. , 1998), Longman Dictionary of Contemporary English 2For a deeper discussion and classification of Porter stemmers errors, see (Krovetz, 1993).
The simplest model of compound noun disambiguation compares the frequencies of the two competing analyses and opts for the most frequent one (Pustejovsky et al. , Model Alta BNC Baseline 63.93 63.93 f (n1;n2) : f (n2;n3) 77.86 66.39 f (n1;n2) : f (n1;n3) 78.68# 65.57 f (n1;n2)= f (n1) : f (n2;n3)= f (n2) 68.85 65.57 f (n1;n2)= f (n2) : f (n2;n3)= f (n3) 70.49 63.11 f (n1;n2)= f (n2) : f (n1;n3)= f (n3) 80.32 66.39 f (n1;n2) : f (n2;n3) (NEAR) 68.03 63.11 f (n1;n2) : f (n1;n3) (NEAR) 71.31 67.21 f (n1;n2)= f (n1) : f (n2;n3)= f (n2) (NEAR) 61.47 62.29 f (n1;n2)= f (n2) : f (n2;n3)= f (n3) (NEAR) 65.57 57.37 f (n1;n2)= f (n2) : f (n1;n3)= f (n3) (NEAR) 75.40 68.03# Table 8: Performance of Altavista counts and BNC counts for compound bracketing (data from Lauer 1995) Model Accuracy Baseline 63.93 Best BNC 68.036  Lauer (1995): adjacency 68.90 Lauer (1995): dependency 77.50 Best Altavista 78.686  Lauer (1995): tuned 80.70 Upper bound 81.50 Table 9: Performance comparison with the literature for compound bracketing 1993).
ENGLISH GERMAN CHINESE (Marcus et al. , 1993) (Skut et al. , 1997) (Xue et al. , 2002) TrainSet Section 2-21 Sentences 1-18,602 Articles 26-270 DevSet Section 22 18,603-19,602 Articles 1-25 TestSet Section 23 19,603-20,602 Articles 271-300 Table 3: Experimental setup.
To determine the target distribution we classified 171 (approximately 5%) randomly selected utterances from the TownInfo data, that were used as a development set.2 In Table 1 we can see that 15.2 % of the trees in the artificial corpus will be NP NSUs.3 4 Data generation We constructed our artificial corpus from sections 2 to 21 of the Wall Street Journal (WSJ) section of the Penn Treebank corpus (Marcus et al., 1993) 2We discarded very short utterances (yes, no, and greetings) since they dont need parsing.
There are cases, though, where the labels consist of several related, but not entirely correlated, properties; examples include mention detectionthe task we are interested in, syntactic parsing with functional tag assignment (besides identifying the syntactic parse, also label the constituent nodes with their functional category, as defined in the Penn Treebank (Marcus et al. , 1993)), and, to a lesser extent, part-of-speech tagging in highly inflected languages.4 The particular type of mention detection that we are examining in this paper follows the ACE general definition: each mention in the text (a reference to a real-world entity) is assigned three types of information:5  An entity type, describing the type of the entity it points to (e.g. person, location, organization, etc)  An entity subtype, further detailing the type (e.g. organizations can be commercial, governmental and non-profit, while locations can be a nation, population center, or an international region)  A mention type, specifying the way the entity is realized  a mention can be named (e.g. John Smith), nominal (e.g. professor), or pronominal (e.g. she).
1 Introduction A number of wide-coverage TAG, CCG, LFG and HPSG grammars (Xia, 1999; Chen et al. , 2005; Hockenmaier and Steedman, 2002a; ODonovan et al. , 2005; Miyao et al. , 2004) have been extracted from the Penn Treebank (Marcus et al. , 1993), and have enabled the creation of widecoverage parsers for English which recover local and non-local dependencies that approximate the underlying predicate-argument structure (Hockenmaier and Steedman, 2002b; Clark and Curran, 2004; Miyao and Tsujii, 2005; Shen and Joshi, 2005).
Typically, the local context around the 215 word to be sense-tagged is used to disambiguate the sense (Yarowsky, 1993), and it is common for linguistic resources such as WordNet (Li et al. , 1995; Mihalcea and Moldovan, 1998; Ramakrishnan and Prithviraj, 2004), or bilingual data (Li and Li, 2002) to be employed as well as more longrange context.
TB TBR JJ, JJR, JJS JJ RB,RBR,RBS RB CD, LS CD CC CC DT, WDT, PDT DT FW FW MD, VB, VBD, VBG, VBN, VBP, VBZ, VH, VHD, VHG, VHN, VHP, VHZ MD NN, NNS, NP, NPS NN PP, WP, PP$, WP$, EX, WRB PP IN, TO IN POS PO RP RP SYM SY UH UH VV, VVD, VVG, VVN, VVP, VVZ VB (Marcus et al. , 1993).
RECALL F-SCORE Brackets 89.17 87.50 88.33 Dependencies 96.40 96.40 96.40 Brackets, revised 97.56 98.03 97.79 Dependencies, revised 99.27 99.27 99.27 Table 1: Agreement between annotators few weeks, and increased to about 1000 words per hour after gaining more experience (Marcus et al. , 1993).
The English experiments were performed on the Penn Treebank (Marcus et al., 1993), using a standard set of head-selection rules (Yamada and Matsumoto, 2003) to convert the phrase structure syntax of the Treebank to a dependency tree representation.6 We split the Treebank into a training set (Sections 221), a development set (Section 22), and several test sets (Sections 0,7 1, 23, and 24).
1 Introduction The last few decades have seen the emergence of multiple treebanks annotated with different grammar formalisms, motivated by the diversity of languages and linguistic theories, which is crucial to the success of statistical parsing (Abeille et al., 2000; Brants et al., 1999; Bohmova et al., 2003; Han et al., 2002; Kurohashi and Nagao, 1998; Marcus et al., 1993; Moreno et al., 2003; Xue et al., 2005).
1 Introduction The probabilistic relation between verbs and their arguments plays an important role in modern statistical parsers and supertaggers (Charniak 1995, Collins 1996/1997, Joshi and Srinivas 1994, Kim, Srinivas, and Trueswell 1997, Stolcke et al. 1997), and in psychological theories of language processing (Clifton et al. 1984, Ferfeira & McClure 1997, Gamsey et al. 1997, Jurafsky 1996, MacDonald 1994, Mitchell & Holmes 1985, Tanenhaus et al. 1990, Trueswell et al. 1993).
1 To train their system, R&M used a 200k-word chunk of the Penn Treebank Parsed Wall Street Journal (Marcus et al. , 1993) tagged using a transformation-based tagger (Brill, 1995) and extracted base noun phrases from its parses by selecting noun phrases that contained no nested noun phrases and further processing the data with some heuristics (like treating the possessive marker as the first word of a new base noun phrase) to flatten the recursive structure of the parse.
Much research has been done to improve tagging accuracy using several different models and methods, including: hidden Markov models (HMMs) (Kupiec, 1992), (Charniak et al. , 1993); rule-based systems (Brill, 1994), (Brill, 1995); memory-based systems (Daelemans et al. , 1996); maximum-entropy systems (Ratnaparkhi, 1996); path voting constraint systems (Tiir and Oflazer, 1998); linear separator systems (Roth and Zelenko, 1998); and majority voting systems (van Halteren et al. , 1998).
The MBT (Daelemans et al. , 1996) 180 Tagger Type Standard Trigram (Weischedel et al. , 1993) MBT (Daelemans et al. , 1996) Rule-based (Brill, 1994) Maximum-Entropy (Ratnaparkhi, 1996) Full Second-Order HMM SNOW (Roth and Zelenko, 1998) Voting Constraints (Tiir and Oflazer, 1998) Full Second-Order HMM Known Unknown Overall Open/Closed Lexicon?
Introduction Verb subcategorizafion probabilities play an important role in both computational linguistic applications (e.g. Carroll, Minnen, and Briscoe 1998, Charniak 1997, Collins 1996/1997, Joshi and Srinivas 1994, Kim, Srinivas, and Tmeswell 1997, Stolcke et al. 1997) and psycholinguisfic models of language processing (e.g. Boland 1997, Clifton et al. 1984, Ferreira & McClure 1997, Fodor 1978, Garnsey et al. 1997, Jurafsky 1996, MacDonald 1994, Mitchell & Holmes 1985, Tanenhaus et al. 1990, Trueswell et al. 1993).
2.2 Motivation from previous work 2.2.1 Parsing In recent years, the success of statistical parsing techniques can be attributed to several factors, such as the increasing size of computing machinery to accommodate larger models, the availability of resources such as the Penn Treebank (Marcus et al. , 1993) and the success of machine learning techniques for lowerlevel NLP problems, such as part-of-speech tagging (Church, 1988; Brill, 1995), and PPattachment (Brill and Resnik, 1994; Collins and Brooks, 1995).
3.2 Probability structure of the original model We use p to denote the unlexicalized nonterminal corresponding to P, and similarly for li, ri and h. We now present the top-level generation probabilities, along with examples from 4The inclusion of the word feature in the BBN model was due to the work described in (Weischedel et al. , 1993), where word features helped reduce part of speech ambiguity for unknown words.
corpus (Garside et al. , 1987), the Penn Treebank (Marcus et al. , 1993), the SUSANNE corpus (Sampson, 1995), the Spoken English Corpus (Taylor and Knowles, 1988), the Oxford Psycholinguistic Database (Quinlan, 1992), and the "Computer-Usable" version of the Oxford Advanced Learner's Dictionary of Current English (OALDCE; Mitton, 1.9.92).
Parsers Precision(a4 ) Recall(a4 ) a5a7a6 (a4 ) a8KM00 a9 93.45 93.51 93.48 a8Hal00 a9 93.13 93.51 93.32 a8CSCL a9 * 93.41 92.64 93.02 a8TKS00 a9 94.04 91.00 92.50 a8ZST00 a9 91.99 92.25 92.12 a8Dej00 a9 91.87 91.31 92.09 a8Koe00 a9 92.08 91.86 91.97 a8Osb00 a9 91.65 92.23 91.94 a8VB00 a9 91.05 92.03 91.54 a8PMP00 a9 90.63 89.65 90.14 a8Joh00 a9 86.24 88.25 87.23 a8VD00 a9 88.82 82.91 85.76 Baseline 72.58 82.14 77.07 2.2 Data Training was done on the Penn Treebank (Marcus et al. , 1993) Wall Street Journal data, sections 02-21.
Firstly, there is also H(RB) A(ADVP) declined H(VBD) H(VP) the dollar A(DT) H(NN) C(NP-SBJ) H(VP) H(S) Figure 2: A tree with constituents marked the top-down method, which is a version of the algorithm described by Hockenmaier et al (Hockenmaier et al. , 2000), but used for translating into simple (AB) CG rather than the Steedmans Combinatory Categorial Grammar (CCG) (Steedman, 1993).
Previous research has shown that RST trees can play a crucial role in building natural language generation systems (Hovy, 1993; Moore and Paris, 1993; Moore, 1995) and text summarization systems (Marcu, 2000); can be used to increase the naturalness of machine translation outputs (Marcu et al. 2000); and can be used to build essayscoring systems that provide students with discourse-based feedback (Burstein et al. , 2001).
We have observed in several experiments that the number of SuperARVs does not grow signi cantly as training set size increases; the moderate-sized Resource Management corpus (Price et al. , 1988) with 25,168 words produces 328 SuperARVs, compared to 538 SuperARVs for the 1 million word Wall Street Journal (WSJ) Penn Treebank set (Marcus et al. , 1993), and 791 for the 37 million word training set of the WSJ continuous speech recognition task.
2 Detecting Discourse-New Definite Descriptions 2.1 Vieira and Poesio Poesio and Vieira (1998) carried out corpus studies indicating that in corpora like the Wall Street Journal portion of the Penn Treebank (Marcus et al. , 1993), around 52% of DDs are discourse-new (Prince, 1992), and another 15% or so are bridging references, for a total of about 66-67% firstmention.
Since Czech is a language with relatively high degree of word-order freedom, and its sentences contain certain syntactic phenomena, such as discontinuous constituents (non-projective constructions), which cannot be straightforwardly handled using the annotation scheme of Penn Treebank (Marcus et al. , 1993; Linguistic Data Consortium, 1999), based on phrase-structure trees, we decided to adopt for the PCEDT the dependency-based annotation scheme of the Prague Dependency Treebank  PDT (Linguistic Data Consortium, 2001).
In our framework, we employ a simple HMM-based tagger, where the most probable tag sequence, a29a30, given the words, a31, is output (Weischedel et al. , 1993): a29 a30 a20a22a32a34a33a36a35a38a37a39a32a41a40 a42 a43a45a44 a30a47a46 a31a49a48a17a20a22a32a34a33a50a35a38a37a39a32a41a40 a42 a43a45a44 a31 a46a30 a48 a43a51a44 a30 a48 Since we do not have enough data which is manually tagged with part-of-speech tags for our applications, we used Penn Treebank (Marcus et al. , 1994) as our training set.
State-of-the-art statistical parsers trained on the Penn Treebank (PTB) (Marcus et al. , 1993) proS a8a8 a8a8a8 a72a72 a72a72a72 NP-SBJ a16a16a16 a80a80a80the authority VP a16a16a16 a16a16a16a16 a0 a0a0 a64 a64a64 a80a80a80 a80a80a80a80 VBD dropped PP-TMP a8a8 a72a72IN at NP NN midnight NP-TMP NNP Tuesday PP-DIR a8a8 a72a72TO to NP QP a16a16a16 a80a80a80$ 2.80 trillion Figure 1: A sample syntactic structure with function labels.
Most of them were developed for exhaustive parsing, i.e., producing all parse results that are given by the grammar (Matsumoto et al. , 1983; Maxwell and Kaplan, 1993; van Noord, 1997; Kiefer et al. , 1999; Malouf et al. , 2000; Torisawa et al. , 2000; Oepen et al. , 2002; Penn and Munteanu, 2003).
1 Introduction A "pain in the neck" (Sag et al. , 2002) for NLP in languages of the Indo-Aryan family (e.g. Hindi-Urdu, Bangla and Kashmiri) is the fact that most verbs (nearly half of all instances in Hindi) occur as complex predicates multi-word complexes which function as a single verbal unit in terms of argument and event structure (Hook, 1993; Butt and Geuder, 2003; Raina and Mukerjee, 2005).
5 Datasets and Evaluation We train our models with verb instances extracted from three parsed corpora: (1) the Wall Street Journal section of the Penn Treebank (PTB), which was parsed by human annotators (Marcus et al. , 1993), (2) the Brown Laboratory for Linguistic Information Processing corpus of Wall Street Journal text (BLLIP), which was parsed automatically by the Charniak parser (Charniak, 2000), and (3) the Gigaword corpus of raw newswire text (GW), which we parsed ourselves with the Stanford parser.
321 Jensen-Shannon divergence is defined as D(q,r) = 12 parenleftbigg D parenleftbigg q|| q +r2 parenrightbigg +D parenleftbigg r|| q +r2 parenrightbiggparenrightbigg These experiments are a kind of poor mans version of the deterministic annealing clustering algorithm (Pereira et al. , 1993; Rose, 1998), which gradually increases the number of clusters during the clustering process.
After the success in syntactic (Penn TreeBank (Marcus et al. , 1993)) and propositional encodings (Penn PropBank (Palmer et al. , 2005)), more sophisticated semantic data (such as temporal (Pustejovsky et al. , 2003) or opinion annotations (Wiebe et al. , 2005)) and discourse data (e.g. , for anaphora resolution (van Deemter and Kibble, 2000) and rhetorical parsing (Carlson et al. , 2003)) are being generated.
Due to its popularity for unsupervised POS induction research (e.g., Goldberg et al., 2008; Goldwater and Griffiths, 2007; Toutanova and Johnson, 2008) and its often-used tagset, for our initial research, we use the Wall Street Journal (WSJ) portion of the Penn Treebank (Marcus et al., 1993), with 36 tags (plus 9 punctuation tags), and we use sections 00-18, leaving held-out data for future experiments.4 Defining frequent frames as those occurring at 4Even if we wanted child-directed speech, the CHILDES database (MacWhinney, 2000) uses coarse POS tags.
Transformation-based error-driven learning has been applied to a number of natural language problems, including part of speech tagging, prepositional phrase attachment disambiguation, speech generation and syntactic parsing \[Brill, 1992; Brill, 1994; Ramshaw and Marcus, 1994; Roche and Schabes, 1995; Brill and Resnik, 1994; Huang et al. , 1994; Brill, 1993a; Brill, 1993b\].
The traditional method of evaluating similarity in a semantic network by measuring the path length between two nodes (Lee et al. , 1993; Rada et al. , 1989) also captures this, albeit indirectly, when the semantic network is just an IS-A hierarchy: if the minimal path of IS-A links between two nodes is long, that means it is necessary to go high in the taxonomy, to more abstract concepts, in order to find their least upper bound.
1993; Chang et al. , 1992; Collins and Brooks, 1995; Fujisaki, 1989; Hindle and Rooth, 1991; Hindle and Rooth, 1993; Jelinek et al. , 1990; Magerman and Marcus, 1991; Magerman, 1995; Ratnaparkhi et al. , 1994; Resnik, 1993; Su and Chang, 1988).
~ gtPdl= |&.allm~WI.Lqlf IDW,t~lIO, r I~"1~~ ~ II, Mlmulm,  IP, il~,,lllb, l~ ~ I I I I I I I I I 0 200 400 600 800 1000 1200 1400 1600 1800 Article# 2000 Figure 1: Distribution of Tags for the word "about" vs. Article# Training Size(wrds)I Test571190 Size(wrds) I Baseline44478 97.04% Specialized 197.13% Table 10: Performance of Baseline ~ Specialized Model When Tested on Consistent Subset of Development Set 139 POS Tag 35 30 25 2O 15 10 5 0 1 I o. Oho m I I I B ~ m M I I I 2 3 4 Annotator Figure 2: Distribution of Tags for the word "about" vs. Annotator (Weischedel et al. , 1993) provide the results from a battery of "tri-tag" Markov Model experiments, in which the probability P(W,T) of observing a word sequence W = {wl,w2,,wn} together with a tag sequence T = {tl,t2,,tn} is given by: P(TIW)P(W) p(tl)p(t21tl)  H P(tilti-lti-2) p(wilti i=3 Furthermore, p(wilti) for unknown words is computed by the following heuristic, which uses a set of 35 pre-determined endings: p(wilti) p(unknownwordlti ) x p(capitalfeature\[ti) x p(endings, hypenationlti ) This approximation works as well as the MaxEnt model, giving 85% unknown word accuracy(Weischedel et al. , 1993) on the Wall St. Journal, but cannot be generalized to handle more diverse information sources.
Comparison With Previous Work Most of the recent corpus-based POS taggers in the literature are either statistically based, and use Markov Model(Weischedel et al. , 1993, Merialdo, 1994) or Statistical Decision Tree(Jelinek et al. , 1994, Magerman, 1995)(SDT) techniques, or are primarily rule based, such as Drill's Transformation Based Learner(Drill, 1994)(TBL).
Table 3 shows the differences between the treebank~ utilized in (Jelinek et al. , 1994) on the one hand, and in the work reported here, on the other, is Table 4 shows relevant lSFigures for Average Sentence Length ('l~raLuing Corpus) and Training Set Size, for the IBM ManuaLs Corpus, are approximate, and cz~e fzom (Black et aL, 1993a).
In marked contrast to annotated training material for partof-speech tagging, (a) there is no coarse-level set of sense distinctions widely agreed upon (whereas part-of-speech tag sets tend to differ in the details); (b) sense annotation has a comparatively high error rate (Miller, personal communication, reports an upper bound for human annotators of around 90% for ambiguous cases, using a non-blind evaluation method that may make even this estimate overly optimistic); and (c) no fully automatic method provides high enough quality output to support the "annotate automatically, correct manually" methodology used to provide high volume annotation by data providers like the Penn Treebank project (Marcus et al. , 1993).
3 Probability Model This paper takes a "history-based" approach (Black et al. , 1993) where each tree-building procedure uses a probability model p(alb), derived from p(a, b), to weight any action a based on the available context, or history, b. First, we present a few simple categories of contextual predicates that capture any information in b that is useful for predicting a. Next, the predicates are used to extract a set of features from a corpus of manually parsed sentences.
Dialogs Speakers Turns Words Fragments Distinct Words Distinct Words/POS Singleton Words Singleton Words/POS Intonational Phrases Speech Repairs 98 34 6163 58298 756 859 1101 252 350 10947 2396 Table 1: Size of the Trains Corpus 2.1 POS Annotations Our POS tagset is based on the Penn Treebank tagset (Marcus et al. , 1993), but modified to include tags for discourse markers and end-of-turns, and to provide richer syntactic information (Heeman, 1997).
The form of the maximum entropy probability model is identical to the one used in (Berger et al. , 1996; Ratnaparkhi, 1998): k f$(wi,wi-1,wi-2,at~ri) YIj=I Otj p(wilwi-l, wi-2,attri) = Z(Wi-l, wi-2, attri) k to t j=l where wi ranges over V t3 .stop.
We used a maximummatching algorithm and a dictionary compiled from the CTB (Sproat et al. , 1996; Xue, 2001) to do segmentation, and trained a maximum entropy part-ofspeech tagger (Ratnaparkhi, 1998) and TAG-based parser (Bikel and Chiang, 2000) on the CTB to do tagging and parsing.4 Then the same feature extraction and model-training was done for the PDN corpus as for the CTB.
(1) Here has(h,x) is a binary function that returns true if the history h has feature x.Inour experiments, we focused on such information as whether or not a string is found in a dictionary, the length of the string, what types of characters are used in the string, and what part-of-speech the adjacent morpheme is. Given a set of features and some training data, the M.E. estimation process produces a model, which is represented as follows (Berger et al. , 1996; Ristad, 1997; Ristad, 1998): P(f|h)= producttext i  g i (h,f) i Z  (h) (2) Z  (h)= summationdisplay f productdisplay i  g i (h,f) i.