Brill, Eric, "Some advances in transformation-based part of speech tagging," Proceeedings of AAAI-94, 1994.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....parser (parser should accept tagger s tag set) 31 it should have compatible tag sets One extra feature we watch for was to be able to easily modify the tagger to t our speci c data. There are many taggers available out there. We chose to use MXPOST tagger [34] and Brill s POS tagger [4] 5][6][7] as being the ones the most respect our guiding principles described before. MXPOST is a statistical tagger and it was trained on sections from Wall Street Journal corpus. MXPOST is a statistical tagger with a Maximum Entropy model that uses many contextual features (the word contains number, ....

Eric Brill. Some advances in transformation-based part of speech tagging. In Proceedings of the Twelfth National Conference on Arti cial Intelligence, volume 1, pages 722-727, 1994. 92

....although the model can train from any large corpus annotated with POS tags. Since most realistic natural language applications must process words that were never seen before in training data, all experiments in this paper are conducted on test data that include unknown words. Several recent papers(Brill, 1994, Magerman, 1995) have reported 96.5 tagging accuracy on the Wall St. Journal corpus. The experiments in this paper test the hypothesis that better use of context will improve the accuracy. A Maximum Entropy model is well suited for such experiments since it corn bines diverse forms of ....

.... 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 Brill s Transformation Based Learner(Brill, 1994) (TBL) The Maximum Entropy (MaxEnt) tagger presented in this paper combines the advantages of all these methods. It uses a rich feature representation, like TBL and SDT, and generates a tag probability distribution for each word, like Decision Tree and Markov Model techniques. 5The mapping from ....

[Article contains additional citation context not shown here]

Eric Brill. 1994. Some Advances in Transformation-Based Part of Speech Tagging. In Proceedings of the Twelfth National Conference on Artificial Intelligence, volume 1, pages 722-727.

....is trained for the subjectivity classification task using documents from the football domain, how well does it perform when this classifier is transferred to the new domain of politics 4. 2 Part of Speech statistics The second approach uses the output of Brill s part of speech (POS) tagger [3] as the basis for its features. It was anticipated that the POS statistics would reflect the style of the language sufficiently for our learning algorithm to distinguish between different genre classes. A document is represented as a vector of 36 POS features, one for each POS tag, expressed as a ....

Eric Brill. Some advances in transformation-based parts of speech tagging. In AAAI, 1994.

....and applied to tagging, by Bahl and Mercer [9] Derouault and Merialdo [26] and Church [20] These taggers have come to be standard. Nonetheless, the rule based line of taggers has continued to be pursued, most notably by Karlsson, Voutilainen, and colleagues [49, 50, 85, 84, 18] and Brill [15, 16]. There have also been e#orts at learning parts of speech from word distributions, with application to tagging [76, 77] Taggers are currently wide spread and readily available. Those available for free include an HMM tagger implemented at Xerox [23] the Brill tagger, and the Multext tagger ....

Eric Brill. Some advances in transformation-based part of speech tagging. In Proceedings of AAAI-94, 1994.

....POS tagging assigns each word in the text a grammatical category. This phase is necessary as a pre processing stage to collocation analysis and the removal of very common words and closed class words, such as determiners, auxiliaries, conjunctions etc. We use the transformation based algorithm of [4] which yields an overral tagging accuracy of around 96.5 . Stop word removal consists simply of table lookup. Collocation analysis aims at finding phrases which may be selected as representative features of a text segment. The approach used in this module is a POS filtering algorithm adapted from ....

Brill, E. Some advances in transformation-based part of speech tagging. In Proceedings of the 12th National Conference on Artificial Intelligence. Volume 1 (Menlo Park, CA, USA, July 31--Aug. 4 1994), AAAI Press, pp. 722--727.

....last of the modules that create DocumentFeatures mapping directly to text regions is the ePhraseEnhancer module. Its purpose is to locate interesting strings of words in the document corresponding to phrases. Since SEE does not currently provide NLP functionality, such as parts of speech tagging [8], ePhraseEnhancer identifies phrases as lists of capitalized words possibly separated by a short list of common words such as of and the. Though this could be implemented as a regular expression, the document facilities of SEE make it much more efficient to have a specialized module. The ....

E. Brill, "Some Advances in Transformation-Based Part of Speech Tagging," National Conference on Artificial Intelligence, 1994.

....splitter is a perl script which notes the sentence boundaries. These boundaries are added as annotations into the GATE database; the annotation includes the offset of the sentence in the document (the span) and all of the tokens which are constituents of the sentence. tagger The Brill tagger [3] is a part of speech tagger that has been extensively trained on Wall Street Journal Text. It annotates tokens with their part of speech. Since an annotation already exists 2 for each token, more information is simply added to each token annotation thus consolidating information. These parts of ....

Brill, E. 1994. Some advances in transformation-based part of speech tagging. Proceedings

.... approaches are Markov chains [Church, 1989, Charniak et al. 1993, Jelinek, 1985, Merialdo, 1994, Garside et al. 1987, Cutting et al. 1992] neural networks [Schmid, 1994] decision trees IDaclemans et al. 1996, Mtrquez and Rodriguez, 1995] and transformation based error driven learning [Brill, 1994]. The most widely used methods for POS tagging are stochastic methods based on fixed order Markov chains models and Hidden Markov models. However, the complexity of Markov chains grows exponen tially with its order L, and hence only low order Markov chains could be considered in practical ....

Brill, E. (1994). Some advances in transformation-based part of speech tagging. In Proceedings of AAAI9J, page 6.

....no match between the subject areas they provide and the knowledge base. Abstracts are processed by the Keyword Extractor module. The output of the module is for each abstract a list of keywords and the number of times that they occur. The core of the Keyword Extractor is a Tagger written by Eric Brill [Brill, 1994; 1995] The other parts are the preprocessor which prepares the data as we have it for processing by Brill s Tagger and the Postprocessor which compiles the output of the tagger into the form that we need. Currently the Postprocessor is relatively simple. It extracts all the nouns and then ....

Eric Brill. Some advances in transformation-based part of speech taggingi/a/,. In Proceedings of AAAI-9J, pages 722-727, 1994.

....problem, in which each word transition must be labeled as either a segment boundary or a within segment transition. Two natural choices for alternative approaches are decision trees and a transformation based, error driven classifier of the type developed by Eric Brill for other tagging problems [2]. Both of these methods would make it easier to combine diverse input features that are not readily integrated into a single probabilistic language model, e.g. if we wanted to use both POS and word identity for each word. Our approach, on the other hand, has the advantage of simplicity and ....

E. Brill. Some advances in transformation-based part of speech tagging. In Proceedings of the 12th National Conference on Artificial Intelligence, Seattle, WA, 1994. AAAI Press.

....act as links between sentences of one speaker but are semantically empty, e.g. and then) D : lexicalized filled pauses (e.g. you know) E : editing terms (within repairs, e.g. I mean) F : non lexicalized filled pauses (e.g. uh, um) 5.2. 1 POS Tagger We trained Brill s rule based POS tagger [1] on the Penn Treebank SwitchBoard corpus with POS and disfluency annotations [13] We replaced the tags in the regions of [C] D] E] and [F] with the tags CO, DM, ET, and UH, respectively. The entire tag set comprises 42 different POS tags. We trained the POS tagger in three phases, using ....

E. Brill. Some advances in transformation-based part of speech tagging. In Proceeedings of AAAI-94, 1994.

....of a word in the document. This approach was used as a baseline to determine how well a standard keyword based learner performs on this task. This approach led to feature vectors that are large and sparse. 5. 2 Part of Speech The second approach uses the output of Brill s part of speech tagger [2] as the basis for its features. It was anticipated that the POS statistics would re ect the style of the language suciently for our learning algorithm to distinguish between di erent genre classes. A document is represented as a vector of 36 POS features, one for each POS tag, expressed as a ....

Eric Brill. Some advances in transformation-based parts of speech tagging. In AAAI, 1994.

....probability distribution does not change substancially if we condition it on preceding subsequences of length greater than L. This feature can be nd in may applications related with natural language procesing such as speech recognition [Jelinek, 1985] Nadas, 1984] and part of speech tagging [Brill, 1994], Merialdo, 1994] An improved model of Markov chains has been developed by Dana Ron [Ron, 1996] in 1996. This model is a subclass of PFAs (Probabilistic Finite Automatas) called PSAs (Probabilistic Sux Automatas) A PSA is hence a variant order L Markov chain, in which the order, of ....

Brill, E. (1994). Some advances in transformation-based part of speech tagging. In Proceedings of AAAI94, page 6.

....is indicative of its subjectivity [10] In using elements of the language style as features, rather than language content we hope to produce a classi er capable of generalizing well to unseen domains. In particular, we rst processed the documents using Brill s Parts Of Speech (POS) tagger [1], and then represented a document as the the fraction of words for each POS. We then used C4.5 to learn a decision tree based on these POS features. While a hybrid approach using both word and lexical features is probably the best overall solution, in these preliminary experiments our classi er ....

Eric Brill. Some advances in transformation-based parts of speech tagging. In AAAI-94.

.... Transformation Based Learning is a learning method that finds a set of rules that transforms the corpus from a baseline annotation so as to minimize the number of errors (we will refer to the system with TBL below) A tagger generator using this learning method is described in (Brill, 1992; Brill, 1994). The implementation that we use is Eric Brill s publicly available set of C programs and Perl scripts. 17 When training, this system starts with a baseline corpus annotation A 0 . In A 0 , each known word is tagged with its most likely tag in the training set, and each unknown word is tagged as ....

Brill, E. 1994. Some advances in transformation-based part-of-speech tagging. In Proceedings AAAI'94.

....Furthermore, they had to satisfy the following conditions: ffl ability to handle large tagset ffl ability to tag unknown words ffl ability to induce lexicon from training data ffl being robust 3.1.1. RBT The Rule Based Tagger was written by Eric Brill of John Hopkins University (Brill, 1992; Brill, 1994; Brill, 1995) The tagger starts with a base annotation of the corpus, and searches for a sequence of transformation rules that repair errors. The base annotation is to assign each word its most frequent tag. Unknown words are initialised as nouns; the tagger first learns a set of rules for ....

Brill, Eric, 1994. Some advances in transformation-based part-of-speech tagging. In Proc. of AAAI'94..

....information in contexts, and so they cannot capture lexical information which is necessary for resolving some morphological ambiguity. Some recent works have reported that tagging accuracy could be improved by using lexical information in their models such as the transformation based patch rules(Brill, 1994), the maximum entropy model(Ratnaparkhi, 1996) the statistical lexical rules(Lee et al. 1999) the HMM considering multi words(Kim, 1996) the selectively lexicalized HMM(Kim et al. 1999) and so on. In the previous works(Kim, 1996) Kim et al. 1999) however, their HMMs were lexicalized ....

E. Brill. 1994. Some Advances in Transformation-Based Part of Speech Tagging. In ##### ## ### #### ##### ##### ## ######### #####################, 722-727.

....between part of speech tags sets. This paper shall propose a method for producing lexical mappings based on corpus evidence. It is based on the existence of large scale lexical annotation tools such as part of speech taggers and sense taggers, several of which have now been developed, for example (Brill, 1994)(Stevenson and Wilks, 1999) The availability of such taggers bring the possibility of automatically annotating large bodies of text. Our proposal is, briefly, to use a pair of taggers with each assigning annotations from the lexical tag sets we are interested in mapping. These taggers can then be ....

....the C5 uses a larger set of 73 tags. A portion of the British National Corpus (BNC) consisting of nearly 9 million words, was used to derive a mapping. One advantage of using the BNC is that it has already been tagged with C5 tags. The first stage was to re tag our corpus using the Brill tagger (Brill, 1994). This produces a bi tagged corpus in which each token has two annotations. For example ponders VBZ VVZ, which represents the token is ponders assigned the Brill tag VBZ and VVZ C5 tag. The bi tagged corpus was used to derive a pair of mappings; the word mapping and the tag mapping. To construct ....

E. Brill. 1994. Some advances in transformationbased part of speech tagging. In AAAI-94, Seattle, WA.

.... rst category consisted of the before mentioned xerox, keper, and d tale systems, augmented with an HMM tagger from the CORRie system (Vosse, 1994) The second group of wotan trained tagger generators contained: mbt, a memory based tagger (Daelemans et al. 1996) MXPOST (Ratnaparkhi, 1996) Eric Brill s rule based system (1994), and TnT, a state of the art HMM implementation (Brants, 2000) 5 Experiments on CGN data 5.1 Data For the experiments a small sample of transcripts from the initial cgn corpus was annotated manually by three independent annotators. After ltering out punctuation from the sample of some 3000 ....

E. Brill. 1994. Some advances in transformation-based part-of-speech tagging.

....It is derived from Bayes Theorem: P (H D) P (H) Theta P (DjH) where H is the hypothesis and D is the data. 1 These publicly available texts include a variety of works such as Virgil s Aeneid and Emily Bronte s Wuthering Heights. A rule based part of speech tagger was used to tag these texts [3]. An optimal code for an event E with probability P (E) has message length ML(E) Gamma log 2 (P (E) Hence, the message length for a hypothesis and the data is: ML(H D) ML(H) ML(DjH) which corresponds to the above mentioned two part message. 3.2 Encoding a Sentence We now describe our ....

Brill, E., Some Advances in Transformation-Based Part-of-Speech Tagging. In Proc. of the Twelfth National Conference on Artificial Intelligence, Seattle, Washington, 722-727, 1994.

....detection and removal Spoken language contains a significant amount of false starts, repetitions, filled pauses, discourse markers and speech repairs. Our goal is to detect and remove those to make the summary more readable for the user. We trained a version of Brill s part of speech (POS) tagger [24] which marks filled pauses, editing terms, discourse markers, and noninformational conjunctions. Further, we use a decision tree [25] to determine false starts, and a script based repetition filter to eliminate the majority of speech repairs. Sentence boundary detection Unlike written language, ....

Eric Brill, "Some advances in transformation-based part of speech tagging," in Proceeedings of AAAI-94, 1994.

....neighboring morphemes regardless of their grammatical functions is not enough for the morpheme level POS disambiguation. Recently, rule based approaches are re studied to cope with the limitations of statistical approaches by learning the tagging rules automatically from the corpus [Brill, 1992, Brill, 1994] Some systems even perform the POS tagging as part of syntactic analysis process [Voutilainen, 1995] However, the rule based approaches alone are in general not robust to handle the unknown words, and is not flexible to adjust to the new tag sets and languages. Also the performance is usually ....

E. Brill. Some advances in transformation-based part-of-speech tagging. In Proceedings of the AAAI-94, 1994.

....words and punctuation, identifying numbers, and so on. b) Sentence Splitting: The sentence splitter identifies sentence boundaries (c) Part of Speech (POS) Tagging: The POS tagger assigns to each token in the input one of 48 POS tags . We have used a slightly modified version of the Brill tagger [6]. d) Morphological Analysis: After POS tagging, all nouns and verbs are passed to the morphological analyser which returns the root and suffix of each word. For example, a plural noun like boxes will be analysed as box s , and an inflected verb form like framing will be analysed as frame ....

E. Brill. Some advances in transformation-based part of speech tagging. In Proceedings of the Twelfth National Conference on AI (AAAI-94), Seattle, Washington, 1994.

....the tag to O2A if adding the prefix nej results in a word 3.2.2 LEARNING CONTEXTUAL CUES The second stage of training is learning rules to improve tagging accuracy based on contextual cues. These rules operate on individual word tokens. 4 We use the same names of files and variables as Eric Brill in the rule based POS tagger s documentation. TAGGED CORPUS manually tagged training corpus, UNTAGGED CORPUS collection of all untagged texts, LEXRULEOUTFILE the list of transformations to determine the most likely tag for unknown words, TAGGED CORPUS 2 manually tagged training ....

Eric Brill. 1994. Some Advances in Transformation --Based Part of Speech Tagging. In: Proceedings of the Twelfth National Conference on Artificial Intelligence.

....POS tags associated with each word, and local constraints on co occurrence of POS tags. These taggers are local in the sense that they use information from a limited context in deciding which tag(s) to choose for each word in a text. As is well known, these taggers (for example, Church 1988, Brill 1994] are quite successful. By associating each word with a unique tag, tagging helps in disambiguating words; hence tagging can provide information on ways in which each word is used. The tagger that we use is a N gram tagger (similar to [Church 1988] and uses the tagset (40 tags) from the Penn ....

Eric Brill. Some Advances in Transformation-Based Part of Speech Tagging In Proceedings of AAAI, 1994.

.... 36, 50, 61, 62, 81, 82, 84, 116, 117, 118, 129, 143, 144, 148, 200] Tagging [10, 19, 28, 56, 57, 66, 90, 91, 124, 125, 126, 131, 138, 153, 163, 168, 188] HMMs [21, 22, 23, 24, 25, 49, 64, 67, 78, 115, 119, 155, 157, 160, 161] Search [156] The Inside Outside Algorithm [85, 86, 136, 137] Regression [20, 30, 29, 38, 41, 42, 45, 46, 154, 162] Partial Parsing [6, 7, 8, 9, 11, 37, 43, 47, 48, 51, 52, 53, 57, 58, 112, 65, 69, 70, 71, 72, 73, 74, 75, 76, 88, 100, 101, 102, 103, 104, 107, 110, 113, 114, 120, 121, 127, 132, 133, 134, 140, 142, 145, 147, 149, 152, 163, 164, 165, 166, 169, 178, 182, 186, 190, 191, 192, 194, 195, 196, 197] ....

Eric Brill. Some advances in transformation-based part of speech tagging. In Proceedings of AAAI94, 1994.

....and applied to tagging, by Bahl and Mercer [9] Derouault and Merialdo [26] and Church [20] These taggers have come to be standard. Nonetheless, the rule based line of taggers has continued to be pursued, most notably by Karlsson, Voutilainen, and colleagues [49, 50, 85, 84, 18] and Brill [15, 16]. There have also been efforts at learning parts of speech from word distributions, with application to tagging [76, 77] Taggers are currently wide spread and readily available. Those available for free include an HMM tagger implemented at Xerox [23] the Brill tagger, and the Multext tagger ....

Eric Brill. Some advances in transformation-based part of speech tagging. In Proceedings of AAAI-94, 1994.

....information in contexts, and so they cannot capture lexical information which is necessary for resolving some morphological ambiguity. Some recent works have reported that tagging accuracy could be improved by using lexical information in their models such as the transformation based patch rules(Brill, 1994), the maximum entropy model(Ratnaparkhi, 1996) the statistical lexical rules(Lee et al. 1999) the HMM considering multi words(Kim, 1996) the selectively lexicalized HMM(Kim et al. 1999) and so on. In the previous works(Kim, 1996) Kim et al. 1999) however, their HMMs were lexicalized ....

E. Brill. 1994. Some Advances in Transformation-Based Part of Speech Tagging. In Proc. of the 12th Nat'l Conf. on Articial Intelligence(AAAI-94), 722-727.

....be extended to act as a repository for more than just Web page data. Possibilities are: Usenet articles, XML, PostScript, PDF, and L A T E Xdocument formats. Information Extraction Methods The cache should support a more extensive range of techniques, such as stemming of terms [20] tagging [4]; and methods based upon syntactic pattern matching [22] Document Pro le Management A more extensive set of API objects are needed to represent document instances within the cache. At present, only the URLData object exists. We are considering the use of a DocumentData class as the root of a ....

Brill, E. Some Advances in Transformation-Based Part of Speech Tagging. In Proceedings of the Twelfth National Conference on Articial Intelligence (AAAI94) (1994).

....1 There are now numerous systems for automatic assignment of parts of speech ( tagging ) employing many different machine learning methods. Among recent top performing methods are Hidden Markov Models (Brants 2000) maximum entropy approaches (Ratnaparkhi 1996) and transformation based learning (Brill 1994). An overview of these and other approaches can be found in Manning and Schtze (1999, ch. 10) However, all these methods use largely the same information sources for tagging, and often almost the same features as well, and as a consequence they also offer very similar levels of performance. This ....

Brill, Eric. 1994. Some Advances in TransformationBased Part of Speech Tagging. Proceedings of AAAI, Vol. 1, pp. 722--727.

....first, and based on the context, the part of speech tagger might eliminate the B (adverb) and N (noun) parse nodes but not the A (adjective) 1.00000:N first.SNz 111 116: first ] 1.00000:B first.6Bz 111 116: first ] 1. 00000:A first.6Az 111 116: first ] Experiments were run using Eric Brill s (1994) freely available rule based part of speech tagger. The fact test suite, which consists of 43 short sentences for a total of 183 words, was run without tagging, with Brill best tagging, and with Brill n best tagging. The training sets used were as provided with the downloaded tagger: the Brown ....

....in French, where heavily inflected forms (such as tlphonassions) must be converted back to their root form (tlphoner) before derivational rules can be applied. Some facts about the French language described by Guillet (1990) are exploited to restrict the investigated parts of speech. See also Brill, 1994. Then the program attempts to derive each lexical entry from known words: For each suffix matching the tail of the root form (prefix matching the head of the word) it attempts to find a lexical entry which matches the head (tail) of the root form and has the part of speech rhs pos of. So, for ....

[Article contains additional citation context not shown here]

Brill, E. (1994). Some advances in transformation-based part of speech tagging. In Proceedings of the Twelfth National Conference on Artificial Intelligence. pp. 722-727. Menlo Park, CA: AAAI Press and Cambridge, MA: MIT Press.

.... word segmentation, there is either no explicit theory learnt, as when neural networks (Rumelhart and McClelland, 1986) or lazy learning (Daelamans et al. 1997) are used, or the derived theories are less sophisticated and do not use any abstractions of the word constituents found in data (Brill, 1994; Mikheev, 1997) The work described here adds to the relatively small but growing number of methods in natural language processing that use little or no annotation (Collins and Singer, 1999; Riloff and Jones, 1999; Thompson et al. 1999) 1.2. Supervised ILP Learning of Morphology Inductive ....

....GA ILP Learning The work described here combines some of the advantages of existing methods for unsupervised learning of word segmentation with the expressiveness of the ILP framework. Unlike several practical tools aiming at word morphology, which limit the length of word constituents (Brill, 1994; Mikheev, 1997; Deligne and Bimbot, 1997) our goal was to allow for unlimited lengths. The frequently made assumption of a maximum of two constituents per word (Pirelli, 1993; Yvon, 1997; Brent et al. 1995) although having its clear limitations, was adopted to keep the data representation ....

[Article contains additional citation context not shown here]

Brill, E.: 1994, `Some Advances in Transformation-Based Part of Speech Tagging'. In: Proc. of the Twelfth National Conference on Artificial Intelligence. pp. 748-- 753, AAI Press/MIT Press.

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Brill, Eric, "Some advances in transformation-based part of speech tagging," Proceeedings of AAAI-94, 1994.

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Eric Brill, "Some advances in transformation-based part of speech tagging," in Proceeedings of AAAI-94, 1994.

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Brill, E. (1994). Some advances in transformation-based part of speech tagging. In Proceedings of the twelfth national conference on artificial intelligence (AAAI-94) (pp. 722--727). Menlo Park, CA: AAAI Press.

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Brill, E. (1994). Some advances in transformation-based part of speech tagging. Twelfth National Conference on Artificial Intelligence (AAAI-94) . Available at: http://www.cs.jhu.edu/~brill/TAGGING_ADVANCES.ps

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Brill, E. Some Advances in Transformation-Based Part of Speech Tagging Publication. In Proceedings of the Twelfth National Conference on Artificial Intelligence. Seattle, WA. 1994.

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Eric Brill. Some advances in transformation-based parts of speech tagging. In AAAI, 1994.

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E. Brill, `Some advances in transformation-based part of speech tagging ', in AAAI, Vol. 1, pp. 722--727, (1994).

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Eric Brill. Some advances in transformation-based parts of speech tagging. In AAAI, 1994.

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Eric Brill. Some advances in transformation-based part of speech tagging. In National Conference on Artificial Intelligence, pages 722--727, 1994.

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E. Brill. Some advances in transformation-based part of speech tagging. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pages 748--753. AAI Press/MIT Press, 1994.

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Eric Brill. Some advances in transformation-based part of speech tagging. In National Conference on Artificial Intelligence, pages 722--727, 1994.

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Eric Brill. Some advances in transformation-based part of speech tagging. In AAAI, Vol. 1, pages 722--727, 1994.

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E. Brill, "Some advances in transformation-based part of speech tagging", in: Proceedings of the Twelfth National Conference on Artificial Intelligence, vol. 1, pp. 722--727, 1994.

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Eric Brill. Some advances in transformation-based parts of speech tagging. In AAAI, 1994.

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"Eric Brill. Some advances in transformation-based part of speech tagging. In In Proceedings of the Twelefth National Conference on Artificial Intelligence, pages 722--727, 1994.

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Eric Brill, "Some advances in transformation-based part of speech tagging," in Proceeedings of AAAI-94, 1994.

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E. Brill. Some advances in transformation-based part of speech tagging. In Twelth National ConferenceonArti#- cial Intelligence, 1994.

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Eric Brill. 1994. Some advances in transformation-based part of speech tagging. In Proceeedings of AAAI-94.

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