Despite the magnitude of recent progress in natural language processing and multilingual language modeling research, the vast majority of NLP research is focused on English and other major languages. This is because recent NLP research is mainly data-driven, and there is more data for resource-rich languages. In particular, Large Language Models (LLM) make use of large unlabeled datasets, a resource that many languages do not have. In this project, we built a new, open-sourced dictionary of Singlish, a contact variety that contains features from English and other local languages and is syntactically, phonologically and lexically distinct from Standard English (Tan, 2010). First, a list of Singlish words was extracted from various online sources. Then using an open Chat-GPT LLM API, the description, including the defintion, part of speech, pronunciation and examples was produced. These were then refined through post processing carried out by a native speaker. The dictionary currently has 1,783 entries and is published under the CC-BY-SA license. The project was carried out with the intention of facilitating future Singlish research and other applications as the accumulation and management of language resources will be of great help in promoting research on the language in the future.

The senses of a word exhibit rich internal structure. In a typical lexicon, this structure is overlooked: A word’s senses are encoded as a list, without inter-sense relations. We present ChainNet, a lexical resource which for the first time explicitly identifies these structures, by expressing how senses in the Open English Wordnet are derived from one another. In ChainNet, every nominal sense of a word is either connected to another sense by metaphor or metonymy, or is disconnected (in the case of homonymy). Because WordNet senses are linked to resources which capture information about their meaning, ChainNet represents the first dataset of grounded metaphor and metonymy.

In this project note we describe our work to make better documentation for the Open Multilingual Wordnet (OMW), a platform integrating many open wordnets. This includes the documentation of the OMW website itself as well as of semantic relations used by the component wordnets. Some of this documentation work was done with the support of the Google Season of Docs. The OMW project page, which links both to the actual OMW server and the documentation has been moved to a new location: https://omwn.org.

This paper describes a new release of the Japanese wordnet. It uses the new global wordnet formats (McCrae et al., 2021) to incorporate a range of new information: orthographic variants (including hiragana, katakana and Latin representations) first described in Kuroda et al. (2011), classifiers, pronouns and exclamatives (Morgado da Costa and Bond, 2016) and many new senses, motivated both from corpus annotation and linking to the TUFs basic vocabulary (Bond et al., 2020). The wordnet has been moved to github and is available at https://bond-lab.github.io/wnja/.

In this paper we describe a new methodology to expand the Abui Wordnet through data collected using the Rapid Word Collection (RWC) method – based on SIL’s Semantic Domains. Using a multilingual sense-intersection algorithm, we created a ranked list of concept suggestions for each domain, and then used the ranked list as a filter to link the Abui RWC data to wordnet. This used translations from both SIL’s Semantic Domain’s structure and example words, both available through SIL’s Fieldworks software and the RWC project. We release both the new mapping of the SIL Semantic Domains to wordnet and an expansion of the Abui Wordnet.

In this paper we examine existing sentiment lexicons and sense-based sentiment-tagged corpora to find out how sense and concept-based semantic relations effect sentiment scores (for polarity and valence). We show that some relations are good predictors of sentiment of related words: antonyms have similar valence and opposite polarity, synonyms similar valence and polarity, as do many derivational relations. We use this knowledge and existing resources to build a sentiment annotated wordnet of English, and show how it can be used to produce sentiment lexicons for other languages using the Open Multilingual Wordnet.

This paper reports on the creation and development of the Tembusu Learner Treebank — an open treebank created from the NTU Corpus of Learner English, unique for incorporating mal-rules in the annotation of ungrammatical sentences. It describes the motivation and development of the treebank, as well as its exploitation to build a new parse-ranking model for the English Resource Grammar, designed to help improve the parse selection of ungrammatical sentences and diagnose these sentences through mal-rules. The corpus contains 25,000 sentences, of which 4,900 are treebanked. The paper concludes with an evaluation experiment that shows the usefulness of this new treebank in the tasks of grammatical error detection and diagnosis.

Singlish is a variety of English spoken in Singapore. In this paper, we share some of its grammar features and how they are implemented in the construction of a computational grammar of Singlish as a branch of English grammar. New rules were created and existing ones from standard English grammar of the English Resource Grammar (ERG) were changed in this branch to cater to how Singlish works. In addition, Singlish lexicon was added into the grammar together with some new lexical types. We used Head-driven Phrase Structure Grammar (HPSG) as the framework for this project of a creating a working computational grammar. As part of building the language resource, we also collected and formatted some data from the internet as part of a test suite for Singlish. Finally, the computational grammar was tested against a set of gold standard trees and compared with the standard English grammar to find out how well the grammar fares in analysing Singlish.

This paper describes the development of an online lexical resource to help detection systems regulate and curb the use of offensive words online. With the growing prevalence of social media platforms, many conversations are now conducted on- line. The increase of online conversations for leisure, work and socializing has led to an increase in harassment. In particular, we create a specialized sense-based vocabulary of Japanese offensive words for the Open Multilingual Wordnet. This vocabulary expands on an existing list of Japanese offen- sive words and provides categorization and proper linking to synsets within the multilingual wordnet. This paper then discusses the evaluation of the vocabulary as a resource for representing and classifying offensive words and as a possible resource for offensive word use detection in social media.

The Global Wordnet Formats have been introduced to enable wordnets to have a common representation that can be integrated through the Global WordNet Grid. As a result of their adoption, a number of shortcomings of the format were identified, and in this paper we describe the extensions to the formats that address these issues. These include: ordering of senses, dependencies between wordnets, pronunciation, syntactic modelling, relations, sense keys, metadata and RDF support. Furthermore, we provide some perspectives on how these changes help in the integration of wordnets.

This paper introduces Wn, a new Python library for working with wordnets. Unlike previous libraries, Wn is built from the beginning to accommodate multiple wordnets — for multiple languages or multiple versions of the same wordnet — while retaining the ability to query and traverse them independently. It is also able to download and incorporate wordnets published online. These features are made possible through Wn’s adoption of standard formats and methods for interoperability, namely the WN-LMF schema (Vossen et al., 2013; Bond et al., 2020) and the Collaborative Interlingual Index (Bond et al., 2016). Wn is open-source, easily available, and well-documented.

The Princeton WordNet for the English language has been used worldwide in NLP projects for many years. With the OMW initiative, wordnets for different languages of the world are being linked via identifiers. The parallel development and linking allows new multilingual application perspectives. The development of a wordnet for the German language is also in this context. To save development time, existing resources were combined and recompiled. The result was then evaluated and improved. In a relatively short time a resource was created that can be used in projects and continuously improved and extended.

In this paper we discuss an ongoing effort to enrich students’ learning by involving them in sense tagging. The main goal is to lead students to discover how we can represent meaning and where the limits of our current theories lie. A subsidiary goal is to create sense tagged corpora and an accompanying linked lexicon (in our case wordnets). We present the results of tagging several texts and suggest some ways in which the tagging process could be improved. Two authors of this paper present their own experience as students. Overall, students reported that they found the tagging an enriching experience. The annotated corpora and changes to the wordnet are made available through the NTU multilingual corpus and associated wordnets (NTU-MC).

In this paper we compare Oxford Lexico and Merriam Webster dictionaries with Princeton WordNet with respect to the description of semantic (dis)similarity between polysemous and homonymous senses that could be inferred from them. WordNet lacks any explicit description of polysemy or homonymy, but as a network of linked senses it may be used to compute semantic distances between word senses. To compare WordNet with the dictionaries, we transformed sample entry microstructures of the latter into graphs and cross-linked them with the equivalent senses of the former. We found that dictionaries are in high agreement with each other, if one considers polysemy and homonymy altogether, and in moderate concordance, if one focuses merely on polysemy descriptions. Measuring the shortest path lengths on WordNet gave results comparable to those on the dictionaries in predicting semantic dissimilarity between polysemous senses, but was less felicitous while recognising homonymy.

This paper introduces a new web system that integrates English Grammatical Error Detection (GED) and course-specific stylistic guidelines to automatically review and provide feedback on student assignments. The system is being developed as a pedagogical tool for English Scientific Writing. It uses both general NLP methods and high precision parsers to check student assignments before they are submitted for grading. Instead of generalized error detection, our system aims to identify, with high precision, specific classes of problems that are known to be common among engineering students. Rather than correct the errors, our system generates constructive feedback to help students identify and correct them on their own. A preliminary evaluation of the system’s in-class performance has shown measurable improvements in the quality of student assignments.

We describe the linking of the TUFS Basic Vocabulary Modules, created for online language learning, with the Open Multilingual Wordnet. The TUFS modules have roughly 500 lexical entries in 30 languages, each with the lemma, a link across the languages, an example sentence, usage notes and sound files. The Open Multilingual Wordnet has 34 languages (11 shared with TUFS) organized into synsets linked by semantic relations, with examples and definitions for some languages. The links can be used to (i) evaluate existing wordnets, (ii) add data to these wordnets and (iii) create new open wordnets for Khmer, Korean, Lao, Mongolian, Russian, Tagalog, Urdua nd Vietnamese

In this paper we discuss the experience of bringing together over 40 different wordnets. We introduce some extensions to the GWA wordnet LMF format proposed in Vossen et al. (2016) and look at how this new information can be displayed. Notable extensions include: confidence, corpus frequency, orthographic variants, lexicalized and non-lexicalized synsets and lemmas, new parts of speech, and more. Many of these extensions already exist in multiple wordnets – the challenge was to find a compatible representation. To this end, we introduce a new version of the Open Multilingual Wordnet (Bond and Foster, 2013), that integrates a new set of tools that tests the extensions introduced by this new format, while also ensuring the integrity of the Collaborative Interlingual Index (CILI: Bond et al., 2016), avoiding the same new concept to be introduced through multiple projects.

WordNet, while one of the most widely used resources for NLP, has not been updated for a long time, and as such a new project English WordNet has arisen to continue the development of the model under an open-source paradigm. In this paper, we detail the second release of this resource entitled “English WordNet 2020”. The work has focused firstly, on the introduction of new synsets and senses and developing guidelines for this and secondly, on the integration of contributions from other projects. We present the changes in this edition, which total over 15,000 changes over the previous release.

We describe the release of a new wordnet for English based on the Princeton WordNet, but now developed under an open-source model. In particular, this version of WordNet, which we call English WordNet 2019, which has been developed by multiple people around the world through GitHub, fixes many errors in previous wordnets for English. We give some details of the changes that have been made in this version and give some perspectives about likely future changes that will be made as this project continues to evolve.

According to George K. Zipf, more frequent words have more senses. We have tested this law using corpora and wordnets of English, Spanish, Portuguese, French, Polish, Japanese, Indonesian and Chinese. We have proved that the law works pretty well for all of these languages if we take - as Zipf did - mean values of meaning count and averaged ranks. On the other hand, the law disastrously fails in predicting the number of senses for a single lemma. We have also provided the evidence that slope coefficients of Zipfian log-log linear model may vary from language to language.

In this paper, we compare a variety of sense-tagged sentiment resources, including SentiWordNet, ML-Senticon, plWordNet emo and the NTU Multilingual Corpus. The goal is to investigate the quality of the resources and see how well the sentiment polarity annotation maps across languages.

This paper introduces a new multilingual lexicon of geographical place names. The names are based on (and linked to) the GeoNames collection. Each location is treated as a new synset, which is linked by instance_hypernym to a small set of supertypes. These supertypes are linked to the collaborative interlingual index, based on mappings from GeoDomainWordnet. If a location is already in the interlingual index, then it is also linked to the entry, using mappings from the Geo-Wordnet. Finally, if GeoNames places the location in a larger location, this is linked using the mero_location link. Wordnets can be built for any language in GeoNames, we give results for those wordnets in the Open Multilingual Wordnet. We discuss how it is mapped and the characteristics of the extracted wordnets.

This paper aims to study auto-hyponymy and auto-troponymy relations (or vertical polysemy) in 11 wordnets uploaded into the new Open Multilingual Wordnet (OMW) webpage. We investigate how vertical polysemy forms polysemy structures (or sense clusters) in semantic hierarchies of the wordnets. Our main results and discoveries are new polysemy structures that have not previously been associated with vertical polysemy, along with some inconsistencies of semantic relations analysis in the studied wordnets, which should not be there. In the case study, we turn attention to polysemy structures in the Estonian Wordnet (version 2.2.0), analyzing them and giving the lexicographers comments. In addition, we describe the detection algorithm of polysemy structures and an overview of the state of polysemy structures in 11 wordnets.

Natural language communication between machines and humans are still constrained. The article addresses a gap in natural language understanding about actions, specifically that of understanding commands. We propose a new method for commonsense inference (grounding) of high-level natural language commands into specific action commands for further execution by a robotic system. The method allows to build a knowledge base that consists of a large set of commonsense inferences. The preliminary results have been presented.

We describe an investigation into the identification and extraction of unrecorded potential lexical items in Japanese text by detecting text passages containing selected language patterns typically associated with such items. We identified a set of suitable patterns, then tested them with two large collections of text drawn from the WWW and Twitter. Samples of the extracted items were evaluated, and it was demonstrated that the approach has considerable potential for identifying terms for later lexicographic analysis.

The paper presents a feature-based model of equivalence targeted at (manual) sense linking between Princeton WordNet and plWordNet. The model incorporates insights from lexicographic and translation theories on bilingual equivalence and draws on the results of earlier synset-level mapping of nouns between Princeton WordNet and plWordNet. It takes into account all basic aspects of language such as form, meaning and function and supplements them with (parallel) corpus frequency and translatability. Three types of equivalence are distinguished, namely strong, regular and weak depending on the conformity with the proposed features. The presented solutions are language-neutral and they can be easily applied to language pairs other than Polish and English. Sense-level mapping is a more fine-grained mapping than the existing synset mappings and is thus of great potential to human and machine translation.

In this paper, we combine methods to estimate sense rankings from raw text with recent work on word embeddings to provide sense ranking estimates for the entries in the Open Multilingual WordNet (OMW). The existing Word2Vec pre-trained models from Polygot2 are only built for single word entries, we, therefore, re-train them with multiword expressions from the wordnets, so that multiword expressions can also be ranked. Thus this trained model gives embeddings for both single words and multiwords. The resulting lexicon gives a WSD baseline for five languages. The results are evaluated for Semcor sense corpora for 5 languages using Word2Vec and Glove models. The Glove model achieves an average accuracy of 0.47 and Word2Vec achieves 0.31 for languages such as English, Italian, Indonesian, Chinese and Japanese. The experimentation on OMW sense ranking proves that the rank correlation is generally similar to the human ranking. Hence distributional semantics can aid in Wordnet Sense Ranking.

Basic-level categories have been shown to be both psychologically significant and useful in a wide range of practical applications. We build a rule-based system to identify basic-level categories in WordNet, achieving 77% accuracy on a test set derived from prior psychological experiments. With additional annotations we found our system also has low precision, in part due to the existence of many categories that do not fit into the three classes (superordinate, basic-level, and subordinate) relied on in basic-level category research.

We aim to support digital humanities work related to the study of sacred texts. To do this, we propose to build a cross-lingual wordnet within the do-main of theology. We target the Collaborative Interlingual Index (CILI) directly instead of each individual wordnet. The paper presents background for this proposal: (1) an overview of concepts relevant to theology and (2) a summary of the domain-associated issues observed in the Princeton WordNet (PWN). We have found that definitions for concepts in this domain can be too restrictive, inconsistent, and unclear. Necessary synsets are missing, with the PWN being skewed towards Christianity. We argue that tackling problems in a single domain is a better method for improving CILI. By focusing on a single topic rather than a single language, this will result in the proper construction of definitions, romanization/translation of lemmas, and also improvements in use of/creation of a cross-lingual domain hierarchy.

Moroccan Darija is a variant of Arabic with many influences. Using the Open Multilingual WordNet (OMW), we compare the lemmas in the Moroccan Darija Wordnet (MDW) with the standard Arabic, French and Spanish ones. We then compared the lemmas in each synset with their translation equivalents. Transliteration is used to bridge alphabet differences and match lemmas in the closest phonological way. The results put figures on the similarity Moroccan Darija has with Arabic, French and Spanish: respectively 42.0%, 2.8% and 2.2%.

We describe preliminary work in the creation of the first specialized vocabulary to be integrated into the Open Multilingual Wordnet (OMW). The NCIt Derived WordNet (ncitWN) is based on the National Cancer Institute Thesaurus (NCIt), a controlled biomedical terminology that includes formal class restrictions and English definitions developed by groups of clinicians and terminologists. The ncitWN is created by converting the NCIt to the WordNet Lexical Markup Framework and adding semantic types. We report the development of a prototype ncitWN and first steps towards integrating it into the OMW.

This paper describes the creation of a new annotated learner corpus. The aim is to use this corpus to develop an automated system for corrective feedback on students’ writing. With this system, students will be able to receive timely feedback on language errors before they submit their assignments for grading. A corpus of assignments submitted by first year engineering students was compiled, and a new error tag set for the NTU Corpus of Learner English (NTUCLE) was developed based on that of the NUS Corpus of Learner English (NUCLE), as well as marking rubrics used at NTU. After a description of the corpus, error tag set and annotation process, the paper presents the results of the annotation exercise as well as follow up actions. The final error tag set, which is significantly larger than that for the NUCLE error categories, is then presented before a brief conclusion summarising our experience and future plans.

We present a novel approach to Computer Assisted Language Learning (CALL), using deep syntactic parsers and semantic based machine translation (MT) in diagnosing and providing explicit feedback on language learners’ errors. We are currently developing a proof of concept system showing how semantic-based machine translation can, in conjunction with robust computational grammars, be used to interact with students, better understand their language errors, and help students correct their grammar through a series of useful feedback messages and guided language drills. Ultimately, we aim to prove the viability of a new integrated rule-based MT approach to disambiguate students’ intended meaning in a CALL system. This is a necessary step to provide accurate coaching on how to correct ungrammatical input, and it will allow us to overcome a current bottleneck in the field — an exponential burst of ambiguity caused by ambiguous lexical items (Flickinger, 2010). From the users’ interaction with the system, we will also produce a richly annotated Learner Corpus, annotated automatically with both syntactic and semantic information.

The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections. A major outcome of our work is the Linguistic Linked Open Data (LLOD) cloud, an LOD (sub-)cloud of linguistic resources, which covers various linguistic databases, lexicons, corpora, terminologies, and metadata repositories. We present and summarize five years of progress on the development of the cloud and of advancements in open data in linguistics, and we describe recent community activities. The paper aims to serve as a guideline to orient and involve researchers with the community and/or Linguistic Linked Open Data.

In this paper we present the ongoing efforts to expand the depth and breath of the Open Multilingual Wordnet coverage by introducing two new classes of non-referential concepts to wordnet hierarchies: interjections and numeral classifiers. The lexical semantic hierarchy pioneered by Princeton Wordnet has traditionally restricted its coverage to referential and contentful classes of words: such as nouns, verbs, adjectives and adverbs. Previous efforts have been employed to enrich wordnet resources including, for example, the inclusion of pronouns, determiners and quantifiers within their hierarchies. Following similar efforts, and motivated by the ongoing semantic annotation of the NTU-Multilingual Corpus, we decided that the four traditional classes of words present in wordnets were too restrictive. Though non-referential, interjections and classifiers possess interesting semantics features that can be well captured by lexical resources like wordnets. In this paper, we will further motivate our decision to include non-referential concepts in wordnets and give an account of the current state of this expansion.

Supervised methods for Word Sense Disambiguation (WSD) benefit from high-quality sense-annotated resources, which are lacking for many languages less common than English. There are, however, several multilingual parallel corpora that can be inexpensively annotated with senses through cross-lingual methods. We test the effectiveness of such an approach by attempting to disambiguate English texts through their translations in Italian, Romanian and Japanese. Specifically, we try to find the appropriate word senses for the English words by comparison with all the word senses associated to their translations. The main advantage of this approach is in that it can be applied to any parallel corpus, as long as large, high-quality inter-linked sense inventories exist for all the languages considered.

This paper introduces the motivation for and design of the Collaborative InterLingual Index (CILI). It is designed to make possible coordination between multiple loosely coupled wordnet projects. The structure of the CILI is based on the Interlingual index first proposed in the EuroWordNet project with several pragmatic extensions: an explicit open license, definitions in English and links to wordnets in the Global Wordnet Grid.

This paper describes our attempts to add Indonesian definitions to synsets in the Wordnet Bahasa (Nurril Hirfana Mohamed Noor et al., 2011; Bond et al., 2014), to extract semantic relations between lemmas and definitions for nouns and verbs, such as synonym, hyponym, hypernym and instance hypernym, and to generally improve Wordnet. The original, somewhat noisy, definitions for Indonesian came from the Asian Wordnet project (Riza et al., 2010). The basic method of extracting the relations is based on Bond et al. (2004). Before the relations can be extracted, the definitions were cleaned up and tokenized. We found that the definitions cannot be completely cleaned up because of many misspellings and bad translations. However, we could identify four semantic relations in 57.10% of noun and verb definitions. For the remaining 42.90%, we propose to add 149 new Indonesian lemmas and make some improvements to Wordnet Bahasa and Wordnet in general.

In languages such as Chinese, classifiers (CLs) play a central role in the quantification of noun-phrases. This can be a problem when generating text from input that does not specify the classifier, as in machine translation (MT) from English to Chinese. Many solutions to this problem rely on dictionaries of noun-CL pairs. However, there is no open large-scale machine-tractable dictionary of noun-CL associations. Many published resources exist, but they tend to focus on how a CL is used (e.g. what kinds of nouns can be used with it, or what features seem to be selected by each CL). In fact, since nouns are open class words, producing an exhaustive definite list of noun-CL associations is not possible, since it would quickly get out of date. Our work tries to address this problem by providing an algorithm for automatic building of a frequency based dictionary of noun-CL pairs, mapped to concepts in the Chinese Open Wordnet (Wang and Bond, 2013), an open machine-tractable dictionary for Chinese. All results will released under an open license.

In this paper, we describe a new and improved Global Wordnet Grid that takes advantage of the Collaborative InterLingual Index (CILI). Currently, the Open Multilingal Wordnet has made many wordnets accessible as a single linked wordnet, but as it used the Princeton Wordnet of English (PWN) as a pivot, it loses concepts that are not part of PWN. The technical solution to this, a central registry of concepts, as proposed in the EuroWordnet project through the InterLingual Index, has been known for many years. However, the practical issues of how to host this index and who decides what goes in remained unsolved. Inspired by current practice in the Semantic Web and the Linked Open Data community, we propose a way to solve this issue. In this paper we define the principles and protocols for contributing to the Grid. We tested them on two use cases, adding version 3.1 of the Princeton WordNet to a CILI based on 3.0 and adding the Open Dutch Wordnet, to validate the current set up. This paper aims to be a call for action that we hope will be further discussed and ultimately taken up by the whole wordnet community.

Optimally, a translated text should preserve information while maintaining the writing style of the original. When this is not possible, as is often the case with figurative speech, a common practice is to simplify and make explicit the implications. However, in our investigations of translations from English to another language, English-to-Chinese texts were often found to include idiomatic expressions (usually in the form of Chengyu 成è ̄) where there were originally no idiomatic, metaphorical, or even figurative expressions. We have created an initial small lexicon of Chengyu, with which we can use to find all occurrences of Chengyu in a given corpus, and will continue to expand the database. By examining the rates and patterns of occurrence across four genres in the NTU Multilingual Corpus, a resource may be created to aid machine translation or, going further, predict Chinese translational trends in any given genre.

Sense-annotated parallel corpora play a crucial role in natural language processing. This paper introduces our progress in creating such a corpus for Asian languages using English as a pivot, which is the first such corpus for these languages. Two sets of tools have been developed for sequential and targeted tagging, which are also easy to set up for any new language in addition to those we are annotating. This paper also briefly presents the general guidelines for doing this project. The current results of monolingual sense-tagging and multilingual linking are illustrated, which indicate the differences among genres and language pairs. All the tools, guidelines and the manually annotated corpus will be freely available at compling.ntu.edu.sg/ntumc.

In this paper we describe the construction of an illustrated Japanese Wordnet. We bootstrap the Wordnet using existing multiple existing wordnets in order to deal with the ambiguity inherent in translation. We illustrate it with pictures from the Open Clip Art Library.

After a long history of compilation of our own lexical resources, EDR Japanese/English Electronic Dictionary, and discussions with major players on development of various WordNets, Japanese National Institute of Information and Communications Technology started developing the Japanese WordNet in 2006 and will publicly release the first version, which includes both the synset in Japanese and the annotated Japanese corpus of SemCor, in June 2008. As the first step in compiling the Japanese WordNet, we added Japanese equivalents to synsets of the Princeton WordNet. Of course, we must also add some synsets which do not exist in the Princeton WordNet, and must modify synsets in the Princeton WordNet, in order to make the hierarchical structure of Princeton synsets represent thesaurus-like information found in the Japanese language, however, we will address these tasks in a future study. We then translated English sentences which are used in the SemCor annotation into Japanese and annotated them using our Japanese WordNet. This article describes the overview of our project to compile Japanese WordNet and other resources which relate to our Japanese WordNet.

We describe various syntactic and semantic conditions for finding abstractnouns which refer to concepts of adjectives from a text, in an attempt to explore the creation of a thesaurus from text. Depending on usages, six kinds of syntactic patterns are shown. In the syntactic and semantic conditions an omission of an abstract noun is mainly used, but in addition, various linguistic clues are needed. We then compare our results with synsets of Japanese WordNet. From a viewpoint of Japanese WordNet, the degree of agreement of ?Attribute? between our data and Japanese WordNet was 22%. On the other hand, the total number of differences of obtained abstract nouns was 267. From a viewpoint of our data,the degree of agreement of abstract nouns between our data and Japanese WordNet was 54%.

Large amounts of training data are essential for training statistical machine translations systems. In this paper we show how training data can be expanded by paraphrasing one side. The new data is made by parsing then generating using a precise HPSG based grammar, which gives sentences with the same meaning, but minor variations in lexical choice and word order. In experiments with Japanese and English, we showed consistent gains on the Tanaka Corpus with less consistent improvement on the IWSLT 2005 evaluation data.

Careful tuning of user-created dictionaries is indispensable when using a machine translation system for computer aided translation. However, there is no widely used standard for user dictionaries in the Japanese/English machine translation market. To address this issue, AAMT (the Asia-Pacific Association for Machine Translation) has established a specification of sharable dictionaries (UTX-S: Universal Terminology eXchange -- Simple), which can be used across different machine translation systems, thus increasing the interoperability of language resources. UTX-S is simpler than existing specifications such as UPF and OLIF. It was explicitly designed to make it easy to (a) add new user dictionaries and (b) share existing user dictionaries. This facilitates rapid user dictionary production and avoids vendor tie in. In this study we describe the UTX-Simple (UTX-S) format, and show that it can be converted to the user dictionary formats for five commercial English-Japanese MT systems. We then present a case study where we (a) convert an on-line glossary to UTX-S, and (b) produce user dictionaries for five different systems, and then exchange them. The results show that the simplified format of UTX-S can be used to rapidly build dictionaries. Further, we confirm that customized user dictionaries are effective across systems, although with a slight loss in quality: on average, user dictionaries improved the translations for 44.8% of translations with the systems they were built for and 37.3% of translations for different systems. In ongoing work, AAMT is using UTX-S as the format in building up a user community for producing, sharing, and accumulating user dictionaries in a sustainable way.

We propose a method to alleviate the problem of referential granularity for Japanese zero pronoun resolution. We use dictionary definition sentences to extract ‘representative’ arguments of predicative definition words; e.g. ‘arrest’ is likely to take police as the subject and criminal as its object. These representative arguments are far more informative than ‘person’ that is provided by other valency dictionaries. They are auto-extracted using both Shallow parsing and Deep parsing for greater quality and quantity. Initial results are highly promising, obtaining more specific information about selectional preferences. An architecture of zero pronoun resolution using these representative arguments is described.

In the LOGON machine translation system where semantic transfer using Minimal Recursion Semantics is being developed in conjunction with two existing broad-coverage grammars of Norwegian and English, we motivate the use of a grammar-specific semantic interface (SEM-I) to facilitate the construction and maintenance of a scalable translation engine. The SEM-I is a theoretically grounded component of each grammar, capturing several classes of lexical regularities while also serving the crucial engineering function of supplying a reliable and complete specification of the elementary predications the grammar can realize. We make extensive use of underspecification and type hierarchies to maximize generality and precision.

Information on subcategorization and selectional restrictions is important for natural language processing tasks such as deep parsing, rule-based machine translation and automatic summarization. In this paper we present a method of adding detailed entries to a bilingual dictionary, based on information in an existing valency dictionary. The method is based on two assumptions: words with similar meaning have similar subcategorization frames and selectional restrictions; and words with the same translations have similar meanings. Based on these assumptions, new valency entries are constructed from words in a plain bilingual dictionary, using entries with similar source-language meaning and the same target-language translations. We evaluate the effects of various measures of similarity in increasing accuracy.

We present a method for combining two bilingual dictionaries to make a third, using one language as a pivot. In this case we combine a Japanese-English dictionary with a Malay-English dictionary, to produce a Japanese-Malay dictionary suitable for use in a machine translation system. Our method differs from previous methods in its use of semantic classes to rank translation equivalents: word pairs with compatible semantic classes are preferred to those with dissimilar classes. We also experiment with the use of two pivot languages. We have made a prototype dictionary of over 75,000 pairs.

In this report we introduce ALT-J/M: a prototype Japanese-to-Malay translation system. The system is a semantic transfer based system that uses the same translation engine as ALT-J/E, a Japanese-to-English system.