DOCSLIB.ORG

Teaching Treebanks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Teaching Treebanks Teaching Treebanking by Martin Volk, Sofia Gustafson-Capková and David Hagstrand (Stockholm University) Heli Uibo (Tartu University) 1. Introduction Treebanks have become valuable resources in natural language processing (NLP) in recent years (Abeillé, 2003). A treebank is a collection of syntactically annotated sentences in which the annotation has been manually checked. The name derives from the fact that syntactic descriptions of sentences often come in the form of tree structures, in particular constituent trees. But treebank annotation has also been done in the framework of dependency grammar and recent annotation has also exceeded syntax towards semantic features such as predicate-argument structures or word senses. A treebank can serve as training corpus for natural language parsers, as repository for linguistic research, or as evaluation corpus for NLP systems. In particular it has been shown (Manning and Schütze, 1999) that natural language parsers which are trained on treebanks (rather than being based on hand-crafted rules and preferences) are more robust and thus more successful for practical applications. Treebanks have also become a necessary resource for many research activities in NLP. But while treebanking has proven to be of high importance for NLP, instruction in this field has been largely missing. Students have learned about treebanks within syntax courses or within general courses on natural language processing or on corpus linguistics. But treebanking is a time-consuming and demanding activity and therefore specific training is essential. We therefore organized an intensive course on “Treebanks: Formats, Tools and Usage” for PhD students in Language Technology from the Nordic countries. The course took place at Stockholm University in March 2004 and was attended by more than 20 participants from Denmark, Estonia, Finland, Iceland, Norway, and Sweden. Due to its success it was repeated in a reduced manner for undergraduate students as part of the summer school on “Empirical Methods in NLP” at Tartu University in August 2004. Again the course was attended by more than 20 participants, this time coming from the Baltic countries, Russia and Ukraine. Participants at the Stockholm University treebanking course The courses introduced the processes involved in creating and exploiting treebanks. They gave an overview of the annotation formats in different treebanks (e.g. the English Penn Treebank, the Swedish Treebank SynTag, the German TIGER Treebank, the Danish Dependency Treebank etc.). And they demonstrated the most important tools used for the creation of treebanks (tree editors), for consistency checking in treebanks and for treebank searches. The general goal was to offer the students hands-on experience in treebanking so that they will be able to participate in treebank projects for their own languages or even help initiating such projects. This report documents these two treebank courses. We summarize the topics covered in the lectures and comment on what we find most central. We describe how we set up the practical exercises and give recommendations for future practice sessions. We also share our observations about how to adapt the material from a treebank course on the PhD level to an undergraduate course. We conclude with some remarks on supporting activities that accompanied both courses and an outlook. 2. Course format The PhD course in Stockholm (2 credit points) consisted of reading seven research papers before the course, 15 hours of lectures and 12 (+ 3) hours of computer labs. Students who were willing to work on an individual research project could obtain 5 credit points. The Treebank course in Tartu (1 credit point) included 8 hours of lectures and 8 hours of hands-on sessions. The core part of the lectures was similar in Stockholm and Tartu. The course in Stockholm additionally contained some guest lectures on different treebank-related topics (training of a parser, spoken language treebanks and the Danish Dependency Treebank), while the course in Tartu provided new research results about parallel treebanks. For the Tartu course, three out of the four practical sessions designed for the Stockholm course were used in a reduced format to address the undergraduate level. 3. The Treebank Lectures Our ambition with the lectures in the treebank course was to cover the most important aspects of treebanking, from the sampling and collection, over annotation and search into future enrichments of treebanks. In addition to this general overview of treebank construction, we wanted to provide insights into previous and ongoing work with specific treebanks. Composition of the lecture series To fulfill the aims defined above, we let one set of lectures cover more general aspects, and to satisfy the requirements of more specific aspects, external speakers with experience from specific projects in the treebanking field were invited. The lectures dealing with general aspects were taught by staff from Stockholm University and covered the following topics: Corpus collection and preparation Treebank tools and treebank search Treebank maintenance Treebanks and discourse information The invited speakers' talks covered the topics of: Training a parser for Swedish on a treebank (Beata Megyesi, KTH Stockholm) Spoken language treebanks (Erhard Hinrichs, University of Tübingen). The Danish Dependency Treebank (Matthias Trautner Kromann, Copenhagen Business School) An introduction plus a closing talk with a glance into the future, completed the lecture series. The lectures were scheduled in the mornings while the afternoons were reserved for practical exercises (see section 3, Practical Treebank Exercises). The lectures covering general aspects of treebanking were distributed over four topics and are briefly described below. 1. Corpus collection and preparation In this lecture we covered questions concerning the sampling and balancing of a corpus as well as how requirements on the sampling can differ with regard to the intended purpose. Examples of the requirements of the SUC corpus (Ejerhed et al.) and its sampling dimensions were shown and discussed. In addition the choice of format for a corpus was touched upon. 2. Treebank tools and searching Two lectures were addressing this topic, covering different categories of tools useful in the construction (graph editors and disambiguators, such as e.g. Annotate [Anno]) as well as search tools, such as e.g. TIGERSearch [Tiger], developed for the German TIGER Treebank and TGrep2 developed for search in the Penn Treebank (PTB) [Penn]. 3. Treebank maintenance This lecture addressed the topic of how to maintain the corpus after finishing a first version. It was discussed how to handle bug reports, how to distribute, update and enrich a treebank, and how to keep the consistency of a treebank. The legal issues were also brought up. The close connection between the construction (including the selection of format) and future addition of new information was stressed. 4. Treebanks and discourse information In this lecture we showed how existing treebanks have been enriched with discourse information. Examples were drawn from the RST Discourse Treebank (Marcu et al., 1999), which is annotated with rhetorical relations and the Penn Discourse Treebank (PDTB) (Miltsakaki et al., 2004) annotated with coherence relations from a discourse parser based on DL-TAG. Examples of tools for discourse annotation, such as RST- tool and Wordfreak were given, and the lecture closed with a discussion of different approaches to discourse structure. In addition to the lectures covering treebanking topics in general, the three invited speakers each gave one talk in the area of their own specific experiences with treebanking. Beáta Megyesi from KTH, Stockholm, gave a talk on her experiments on training a shallow parser for Swedish (Megyesi, 2002), focusing on parsing methods and problems related to parsing. Erhard Hinrichs from University of Tübingen presented experiences from his work with the development of the Tübingen Treebank of German Spontaneous Speech (TüBa-D/S) (Stegmann, 2000), and discussed problems specific for spoken language and dialogue. And Matthias T. Kromann, CBS Copenhagen, gave a talk on his work with the Danish Dependency Treebank (Kromann, 2003), which consists of a part of the Danish PAROLE corpus analyzed with the dependency-based formalism Discontinuous Grammar. Most weight was put on the discussion of the analysis and formalism. The invited speakers, showing practical examples from their own work with treebank development constituted a natural link between the more theoretical underpinnings in the rest of the lectures and the students’ hands-on work during the practical sessions. The reactions of the course participants indicated that the selection of lecture topics for the course was rather well picked. Of course there are topics that could be considered as more “core treebanking”, such as e.g. the actual annotation of trees including the grammatical formalism and the parsing and annotation tools. However, in constructing a treebank, the quality and usability is dependent also on insights gained in the field of corpus construction in general. That is why the topics, such as e.g. sampling, formats and legal issues, have their given place also in a course directed specifically towards treebanking. 4. Practical Treebank Exercises This section describes the different exercises designed for the course, along with descriptions of what was required to set them up in terms
Load more

Recommended publications
  • How Do BERT Embeddings Organize Linguistic Knowledge?
    How Do BERT Embeddings Organize Linguistic Knowledge? Giovanni Puccettiy , Alessio Miaschi? , Felice Dell’Orletta y Scuola Normale Superiore, Pisa ?Department of Computer Science, University of Pisa Istituto di Linguistica Computazionale “Antonio Zampolli”, Pisa ItaliaNLP Lab – www.italianlp.it [email protected], [email protected], [email protected] Abstract et al., 2019), we proposed an in-depth investigation Several studies investigated the linguistic in- aimed at understanding how the information en- formation implicitly encoded in Neural Lan- coded by BERT is arranged within its internal rep- guage Models. Most of these works focused resentation. In particular, we defined two research on quantifying the amount and type of in- questions, aimed at: (i) investigating the relation- formation available within their internal rep- ship between the sentence-level linguistic knowl- resentations and across their layers. In line edge encoded in a pre-trained version of BERT and with this scenario, we proposed a different the number of individual units involved in the en- study, based on Lasso regression, aimed at understanding how the information encoded coding of such knowledge; (ii) understanding how by BERT sentence-level representations is ar- these sentence-level properties are organized within ranged within its hidden units. Using a suite of the internal representations of BERT, identifying several probing tasks, we showed the existence groups of units more relevant for specific linguistic of a relationship between the implicit knowl- tasks. We defined a suite of probing tasks based on edge learned by the model and the number of a variable selection approach, in order to identify individual units involved in the encodings of which units in the internal representations of BERT this competence.
    [Show full text]
  • Treebanks, Linguistic Theories and Applications Introduction to Treebanks
    Treebanks, Linguistic Theories and Applications Introduction to Treebanks Lecture One Petya Osenova and Kiril Simov Sofia University “St. Kliment Ohridski”, Bulgaria Bulgarian Academy of Sciences, Bulgaria ESSLLI 2018 30th European Summer School in Logic, Language and Information (6 August – 17 August 2018) Plan of the Lecture • Definition of a treebank • The place of the treebank in the language modeling • Related terms: parsebank, dynamic treebank • Prerequisites for the creation of a treebank • Treebank lifecycle • Theory (in)dependency • Language (in)dependency • Tendences in the treebank development 30th European Summer School in Logic, Language and Information (6 August – 17 August 2018) Treebank Definition A corpus annotated with syntactic information • The information in the annotation is added/checked by a trained annotator - manual annotation • The annotation is complete - no unannotated fragments of the text • The annotation is consistent - similar fragments are analysed in the same way • The primary format of annotation - syntactic tree/graph 30th European Summer School in Logic, Language and Information (6 August – 17 August 2018) Example Syntactic Trees from Wikipedia The two main approaches to modeling the syntactic information 30th European Summer School in Logic, Language and Information (6 August – 17 August 2018) Pros vs. Cons (Handbook of NLP, p. 171) Constituency • Easy to read • Correspond to common grammatical knowledge (phrases) • Introduce arbitrary complexity Dependency • Flexible • Also correspond to common grammatical
    [Show full text]
  • Senserelate::Allwords - a Broad Coverage Word Sense Tagger That Maximizes Semantic Relatedness
    WordNet::SenseRelate::AllWords - A Broad Coverage Word Sense Tagger that Maximizes Semantic Relatedness Ted Pedersen and Varada Kolhatkar Department of Computer Science University of Minnesota Duluth, MN 55812 USA tpederse,kolha002 @d.umn.edu http://senserelate.sourceforge.net{ } Abstract Despite these difficulties, word sense disambigua- tion is often a necessary step in NLP and can’t sim- WordNet::SenseRelate::AllWords is a freely ply be ignored. The question arises as to how to de- available open source Perl package that as- velop broad coverage sense disambiguation modules signs a sense to every content word (known that can be deployed in a practical setting without in- to WordNet) in a text. It finds the sense of vesting huge sums in manual annotation efforts. Our each word that is most related to the senses answer is WordNet::SenseRelate::AllWords (SR- of surrounding words, based on measures found in WordNet::Similarity. This method is AW), a method that uses knowledge already avail- shown to be competitive with results from re- able in the lexical database WordNet to assign senses cent evaluations including SENSEVAL-2 and to every content word in text, and as such offers SENSEVAL-3. broad coverage and requires no manual annotation of training data. SR-AW finds the sense of each word that is most 1 Introduction related or most similar to those of its neighbors in the Word sense disambiguation is the task of assigning sentence, according to any of the ten measures avail- a sense to a word based on the context in which it able in WordNet::Similarity (Pedersen et al., 2004).
    [Show full text]
  • Deep Linguistic Analysis for the Accurate Identification of Predicate
    Deep Linguistic Analysis for the Accurate Identification of Predicate-Argument Relations Yusuke Miyao Jun'ichi Tsujii Department of Computer Science Department of Computer Science University of Tokyo University of Tokyo [email protected] CREST, JST [email protected] Abstract obtained by deep linguistic analysis (Gildea and This paper evaluates the accuracy of HPSG Hockenmaier, 2003; Chen and Rambow, 2003). parsing in terms of the identification of They employed a CCG (Steedman, 2000) or LTAG predicate-argument relations. We could directly (Schabes et al., 1988) parser to acquire syntac- compare the output of HPSG parsing with Prop- tic/semantic structures, which would be passed to Bank annotations, by assuming a unique map- statistical classifier as features. That is, they used ping from HPSG semantic representation into deep analysis as a preprocessor to obtain useful fea- PropBank annotation. Even though PropBank tures for training a probabilistic model or statistical was not used for the training of a disambigua- tion model, an HPSG parser achieved the ac- classifier of a semantic argument identifier. These curacy competitive with existing studies on the results imply the superiority of deep linguistic anal- task of identifying PropBank annotations. ysis for this task. Although the statistical approach seems a reason- 1 Introduction able way for developing an accurate identifier of Recently, deep linguistic analysis has successfully PropBank annotations, this study aims at establish- been applied to real-world texts. Several parsers ing a method of directly comparing the outputs of have been implemented in various grammar for- HPSG parsing with the PropBank annotation in or- malisms and empirical evaluation has been re- der to explicitly demonstrate the availability of deep ported: LFG (Riezler et al., 2002; Cahill et al., parsers.
    [Show full text]
  • The Procedure of Lexico-Semantic Annotation of Składnica Treebank
    The Procedure of Lexico-Semantic Annotation of Składnica Treebank Elzbieta˙ Hajnicz Institute of Computer Science, Polish Academy of Sciences ul. Ordona 21, 01-237 Warsaw, Poland [email protected] Abstract In this paper, the procedure of lexico-semantic annotation of Składnica Treebank using Polish WordNet is presented. Other semantically annotated corpora, in particular treebanks, are outlined first. Resources involved in annotation as well as a tool called Semantikon used for it are described. The main part of the paper is the analysis of the applied procedure. It consists of the basic and correction phases. During basic phase all nouns, verbs and adjectives are annotated with wordnet senses. The annotation is performed independently by two linguists. Multi-word units obtain special tags, synonyms and hypernyms are used for senses absent in Polish WordNet. Additionally, each sentence receives its general assessment. During the correction phase, conflicts are resolved by the linguist supervising the process. Finally, some statistics of the results of annotation are given, including inter-annotator agreement. The final resource is represented in XML files preserving the structure of Składnica. Keywords: treebanks, wordnets, semantic annotation, Polish 1. Introduction 2. Semantically Annotated Corpora It is widely acknowledged that linguistically annotated cor- Semantic annotation of text corpora seems to be the last pora play a crucial role in NLP. There is even a tendency phase in the process of corpus annotation, less popular than towards their ever-deeper annotation. In particular, seman- morphosyntactic and (shallow or deep) syntactic annota- tically annotated corpora become more and more popular tion. However, there exist semantically annotated subcor- as they have a number of applications in word sense disam- pora for many languages.
    [Show full text]
  • Unified Language Model Pre-Training for Natural
    Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong∗ Nan Yang∗ Wenhui Wang∗ Furu Wei∗† Xiaodong Liu Yu Wang Jianfeng Gao Ming Zhou Hsiao-Wuen Hon Microsoft Research {lidong1,nanya,wenwan,fuwei}@microsoft.com {xiaodl,yuwan,jfgao,mingzhou,hon}@microsoft.com Abstract This paper presents a new UNIfied pre-trained Language Model (UNILM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirec- tional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UNILM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UNILM achieves new state-of- the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm. 1 Introduction Language model (LM) pre-training has substantially advanced the state of the art across a variety of natural language processing tasks [8, 29, 19, 31, 9, 1].
    [Show full text]
  • Building a Treebank for French
    Building a treebank for French £ £¥ Anne Abeillé£ , Lionel Clément , Alexandra Kinyon ¥ £ TALaNa, Université Paris 7 University of Pennsylvania 75251 Paris cedex 05 Philadelphia FRANCE USA abeille, clement, [email protected] Abstract Very few gold standard annotated corpora are currently available for French. We present an ongoing project to build a reference treebank for French starting with a tagged newspaper corpus of 1 Million words (Abeillé et al., 1998), (Abeillé and Clément, 1999). Similarly to the Penn TreeBank (Marcus et al., 1993), we distinguish an automatic parsing phase followed by a second phase of systematic manual validation and correction. Similarly to the Prague treebank (Hajicova et al., 1998), we rely on several types of morphosyntactic and syntactic annotations for which we define extensive guidelines. Our goal is to provide a theory neutral, surface oriented, error free treebank for French. Similarly to the Negra project (Brants et al., 1999), we annotate both constituents and functional relations. 1. The tagged corpus pronoun (= him ) or a weak clitic pronoun (= to him or to As reported in (Abeillé and Clément, 1999), we present her), plus can either be a negative adverb (= not any more) the general methodology, the automatic tagging phase, the or a simple adverb (= more). Inflectional morphology also human validation phase and the final state of the tagged has to be annotated since morphological endings are impor- corpus. tant for gathering constituants (based on agreement marks) and also because lots of forms in French are ambiguous 1.1. Methodology with respect to mode, person, number or gender. For exam- 1.1.1.
    [Show full text]
  • Converting an HPSG-Based Treebank Into Its Parallel Dependency-Based Treebank
    Converting an HPSG-based Treebank into its Parallel Dependency-based Treebank Masood Ghayoomiy z Jonas Kuhnz yDepartment of Mathematics and Computer Science, Freie Universität Berlin z Institute for Natural Language Processing, University of Stuttgart [email protected] [email protected] Abstract A treebank is an important language resource for supervised statistical parsers. The parser induces the grammatical properties of a language from this language resource and uses the model to parse unseen data automatically. Since developing such a resource is very time-consuming and tedious, one can take advantage of already extant resources by adapting them to a particular application. This reduces the amount of human effort required to develop a new language resource. In this paper, we introduce an algorithm to convert an HPSG-based treebank into its parallel dependency-based treebank. With this converter, we can automatically create a new language resource from an existing treebank developed based on a grammar formalism. Our proposed algorithm is able to create both projective and non-projective dependency trees. Keywords: Treebank Conversion, the Persian Language, HPSG-based Treebank, Dependency-based Treebank 1. Introduction treebank is introduced in Section 4. The properties Supervised statistical parsers require a set of annotated of the available dependency treebanks for Persian and data, called a treebank, to learn the grammar of the their pros and cons are explained in Section 5. The target language and create a wide coverage grammar tools and the experimental setup as well as the ob- model to parse unseen data. Developing such a data tained results are described and discussed in Section source is a tedious and time-consuming task.
    [Show full text]
  • Corpus Based Evaluation of Stemmers
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Repository of the Academy's Library Corpus based evaluation of stemmers Istvan´ Endredy´ Pazm´ any´ Peter´ Catholic University, Faculty of Information Technology and Bionics MTA-PPKE Hungarian Language Technology Research Group 50/a Prater´ Street, 1083 Budapest, Hungary [email protected] Abstract There are many available stemmer modules, and the typical questions are usually: which one should be used, which will help our system and what is its numerical benefit? This article is based on the idea that any corpora with lemmas can be used for stemmer evaluation, not only for lemma quality measurement but for information retrieval quality as well. The sentences of the corpus act as documents (hit items of the retrieval), and its words are the testing queries. This article describes this evaluation method, and gives an up-to-date overview about 10 stemmers for 3 languages with different levels of inflectional morphology. An open source, language independent python tool and a Lucene based stemmer evaluator in java are also provided which can be used for evaluating further languages and stemmers. 1. Introduction 2. Related work Stemmers can be evaluated in several ways. First, their Information retrieval (IR) systems have a common crit- impact on IR performance is measurable by test collec- ical point. It is when the root form of an inflected word tions. The earlier stemmer evaluations (Hull, 1996; Tordai form is determined. This is called stemming. There are and De Rijke, 2006; Halacsy´ and Tron,´ 2007) were based two types of the common base word form: lemma or stem.
    [Show full text]
  • Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
    Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell Djamé Seddah1 Farah Essaidi1 Amal Fethi1 Matthieu Futeral1 Benjamin Muller1;2 Pedro Javier Ortiz Suárez1;2 Benoît Sagot1 Abhishek Srivastava1 1Inria, Paris, France 2Sorbonne Université, Paris, France [email protected] Abstract sourced, namely Haitian Creole and Algerian di- alectal Arabic in these cases. No readily avail- We introduce the first treebank for a romanized able parsing and machine translations systems are user-generated content variety of Algerian, a available for such languages. Taking as an ex- North-African Arabic dialect known for its fre- quent usage of code-switching. Made of 1500 ample the Arabic dialects spoken in North-Africa, sentences, fully annotated in morpho-syntax mostly from Morocco to Tunisia, sometimes called and Universal Dependency syntax, with full Maghribi, sometimes Darija, these idioms notori- translation at both the word and the sentence ously contain various degrees of code-switching levels, this treebank is made freely available. with languages of former colonial powers such as It is supplemented with 50k unlabeled sen- French, Spanish, and, to a much lesser extent, Ital- tences collected from Common Crawl and web- ian, depending on the area of usage (Habash, 2010; crawled data using intensive data-mining tech- Cotterell et al., 2014; Saadane and Habash, 2015). niques. Preliminary experiments demonstrate its usefulness for POS tagging and dependency They share Modern Standard Arabic (MSA) as parsing. We believe that what we present in their matrix language (Myers-Scotton, 1993), and this paper is useful beyond the low-resource of course present a rich morphology.
    [Show full text]
  • Lecture 5: Part-Of-Speech Tagging
    CS447: Natural Language Processing http://courses.engr.illinois.edu/cs447 Lecture 5: Part-of-Speech Tagging Julia Hockenmaier [email protected] 3324 Siebel Center POS tagging Tagset: NNP: proper noun CD: numeral, POS tagger JJ: adjective, ... Raw text Tagged text Pierre_NNP Vinken_NNP ,_, 61_CD Pierre Vinken , 61 years old years_NNS old_JJ ,_, will_MD join_VB , will join the board as a the_DT board_NN as_IN a_DT nonexecutive director Nov. nonexecutive_JJ director_NN Nov._NNP 29 . 29_CD ._. CS447: Natural Language Processing (J. Hockenmaier) !2 Why POS tagging? POS tagging is a prerequisite for further analysis: –Speech synthesis: How to pronounce “lead”? INsult or inSULT, OBject or obJECT, OVERflow or overFLOW," DIScount or disCOUNT, CONtent or conTENT –Parsing: What words are in the sentence? –Information extraction: Finding names, relations, etc. –Machine Translation: The noun “content” may have a different translation from the adjective. CS447: Natural Language Processing (J. Hockenmaier) !3 POS Tagging Words often have more than one POS: " -The back door (adjective) -On my back (noun) -Win the voters back (particle) -Promised to back the bill (verb)" The POS tagging task is to determine the POS tag " for a particular instance of a word. " Since there is ambiguity, we cannot simply look up the correct POS in a dictionary. These examples from Dekang Lin CS447: Natural Language Processing (J. Hockenmaier) !4 Defining a tagset CS447: Natural Language Processing (J. Hockenmaier) !5 Defining a tag set We have to define an inventory of labels for the word classes (i.e. the tag set)" -Most taggers rely on models that have to be trained on annotated (tagged) corpora.
    [Show full text]
  • Merging Propbank, Nombank, Timebank, Penn Discourse Treebank and Coreference James Pustejovsky, Adam Meyers, Martha Palmer, Massimo Poesio
    Merging PropBank, NomBank, TimeBank, Penn Discourse Treebank and Coreference James Pustejovsky, Adam Meyers, Martha Palmer, Massimo Poesio Abstract annotates the temporal features of propositions Many recent annotation efforts for English and the temporal relations between propositions. have focused on pieces of the larger problem The Penn Discourse Treebank (Miltsakaki et al of semantic annotation, rather than initially 2004a/b) treats discourse connectives as producing a single unified representation. predicates and the sentences being joined as This paper discusses the issues involved in arguments. Researchers at Essex were merging four of these efforts into a unified responsible for the coreference markup scheme linguistic structure: PropBank, NomBank, the developed in MATE (Poesio et al, 1999; Poesio, Discourse Treebank and Coreference 2004a) and have annotated corpora using this Annotation undertaken at the University of scheme including a subset of the Penn Treebank Essex. We discuss resolving overlapping and (Poesio and Vieira, 1998), and the GNOME conflicting annotation as well as how the corpus (Poesio, 2004a). This paper discusses the various annotation schemes can reinforce issues involved in creating a Unified Linguistic each other to produce a representation that is Annotation (ULA) by merging annotation of greater than the sum of its parts. examples using the schemata from these efforts. Crucially, all individual annotations can be kept 1. Introduction separate in order to make it easy to produce alternative annotations of a specific type of The creation of the Penn Treebank (Marcus et al, semantic information without need to modify the 1993) and the word sense-annotated SEMCOR annotation at the other levels. Embarking on (Fellbaum, 1997) have shown how even limited separate annotation efforts has the advantage of amounts of annotated data can result in major allowing researchers to focus on the difficult improvements in complex natural language issues in each area of semantic annotation and understanding systems.
    [Show full text]