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.

The Grammar Matrix project is a meta-grammar engineering framework expressed in Head-driven Phrase Structure Grammar (HPSG) and Minimal Recursion Semantics (MRS). It automates grammar implementation and is thus a tool and a resource for linguistic hypothesis testing at scale. In this paper, we summarize how the Grammar Matrix grew in the last decade and describe how new additions to the system have made it possible to study interactions between analyses, both monolingually and cross-linguistically, at new levels of complexity.

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 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.

Abstract Meaning Representation (AMR) (Banarescu et al., 2013) is a framework for semantic dependencies that encodes its rooted and directed acyclic graphs in a format called PENMAN notation. The format is simple enough that users of AMR data often write small scripts or libraries for parsing it into an internal graph representation, but there is enough complexity that these users could benefit from a more sophisticated and well-tested solution. The open-source Python library Penman provides a robust parser, functions for graph inspection and manipulation, and functions for formatting graphs into PENMAN notation. Many functions are also available in a command-line tool, thus extending its utility to non-Python setups.

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.

We propose neural models to generate high-quality text from structured representations based on Minimal Recursion Semantics (MRS). MRS is a rich semantic representation that encodes more precise semantic detail than other representations such as Abstract Meaning Representation (AMR). We show that a sequence-to-sequence model that maps a linearization of Dependency MRS, a graph-based representation of MRS, to English text can achieve a BLEU score of 66.11 when trained on gold data. The performance can be improved further using a high-precision, broad coverage grammar-based parser to generate a large silver training corpus, achieving a final BLEU score of 77.17 on the full test set, and 83.37 on the subset of test data most closely matching the silver data domain. Our results suggest that MRS-based representations are a good choice for applications that need both structured semantics and the ability to produce natural language text as output.

Meaning Representation (AMR; Banarescu et al., 2013) encodes the meaning of sentences as a directed graph and Smatch (Cai and Knight, 2013) is the primary metric for evaluating AMR graphs. Smatch, however, is unaware of some meaning-equivalent variations in graph structure allowed by the AMR Specification and gives different scores for AMRs exhibiting these variations. In this paper I propose four normalization methods for helping to ensure that conceptually equivalent AMRs are evaluated as equivalent. Equivalent AMRs with and without normalization can look quite different---comparing a gold corpus to itself with relation reification alone yields a difference of 25 Smatch points, suggesting that the outputs of two systems may not be directly comparable without normalization. The algorithms described in this paper are implemented on top of an existing open-source Python toolkit for AMR and will be released under the same license.

This paper describes PyDelphin: an open-source software library, coded in Python, for working with the resources and results of Deep Linguistic Processing with HPSG (DELPH-IN). These resources and processing results offer rich syntactic and semantic information from linguistically-informed grammars but the original software stack for working with them presents significant technical hurdles for installation and use, particularly for new users and for single experiments. PyDelphin is quick to install and its thorough documentation makes it easy for newcomers to get started. It contains packages that implement a number of DELPH-IN technologies for working with semantic representations, grammar descriptions, tokenization, and corpus databases. It also has client interfaces for related external processors which make it straightforward to integrate these tools into a single workflow. The library has been successfully used in several research projects in topics such as abstractive summarization, machine translation, and natural language generation.

Extracting semi-structured text from scientific writing in PDF files is a difficult task that researchers have faced for decades. In the 1990s, this task was largely a computer vision and OCR problem, as PDF files were often the result of scanning printed documents. Today, PDFs have standardized digital typesetting without the need for OCR, but extraction of semi-structured text from these documents remains a nontrivial task. In this paper, we present a system for the reanalysis of glyph-level PDF-extracted text that performs block detection, respacing, and tabular data analysis for the purposes of linguistic data mining. We further present our reanalyzed output format, which attempts to eliminate the extreme verbosity of XML output while leaving important positional information available for downstream processes.

This is a report on the methods used and results obtained by the UW-Stanford team for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016 on grammatical error detection.This team developed a symbolic grammar-based system augmented with manually defined mal-rules to accommodate and identify instances of highfrequency grammatical errors.System results were entered both for the probabilistic estimation track, where we ranked second, and for the Boolean decision track, where we ranked fourth.

The current release of the ODIN (Online Database of Interlinear Text) database contains over 150,000 linguistic examples, from nearly 1,500 languages, extracted from PDFs found on the web, representing a significant source of data for language research, particularly for low-resource languages. Errors introduced during PDF-totext conversion or poorly formatted examples can make the task of automatically analyzing the data more difficult, so we aim to clean and normalize the examples in order to maximize accuracy during analysis. In this paper we describe a system that allows users to automatically and manually correct errors in the source data in order to get the best possible analysis of the data. We also describe a RESTful service for managing collections of linguistic examples on the web. All software is distributed under an open-source license.

The majority of the world's languages have little to no NLP resources or tools. This is due to a lack of training data ("resources") over which tools, such as taggers or parsers, can be trained. In recent years, there have been increasing efforts to apply NLP methods to a much broader swath of the world's languages. In many cases this involves bootstrapping the learning process with enriched or partially enriched resources. We propose that Interlinear Glossed Text (IGT), a very common form of annotated data used in the field of linguistics, has great potential for bootstrapping NLP tools for resource-poor languages. Although IGT is generally very richly annotated, and can be enriched even further (e.g., through structural projection), much of the content is not easily consumable by machines since it remains "trapped" in linguistic scholarly documents and in human readable form. In this paper, we describe the expansion of the ODIN resource--a database containing many thousands of instances of IGT for over a thousand languages. We enrich the original IGT data by adding word alignment and syntactic structure. To make the data in ODIN more readily consumable by tool developers and NLP researchers, we adopt and extend a new XML format for IGT, called Xigt. We also develop two packages for manipulating IGT data: one, INTENT, enriches raw IGT automatically, and the other, XigtEdit, is a graphical IGT editor.

We describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium. We concentrate in particular on the Dependency Minimal Recursion Semantics (DMRS) formalism, a graph-based representation designed for compositional semantic representation with deep grammars. Our main focus is on English, and specifically English Resource Semantics (ERS) as used in the English Resource Grammar. We first give an introduction to ERS and DMRS and a brief overview of some existing resources and then describe in detail a new repository which has been developed to simplify the use of ERS/DMRS. We explain a number of operations on DMRS graphs which our repository supports, with sketches of the algorithms, and illustrate how these operations can be exploited in application building. We believe that this work will aid researchers to exploit the rich and effective but complex DELPH-IN resources.

This paper presents Xigt, an extensible storage format for interlinear glossed text (IGT). We review design desiderata for such a format based on our own use cases as well as general best practices, and then explore existing representations of IGT through the lens of those desiderata. We give an overview of the data model and XML serialization of Xigt, and then describe its application to the use case of representing a large, noisy, heterogeneous set of IGT.

The majority of the world's languages have little to no NLP resources or tools. This is due to a lack of training data ("resources") over which tools, such as taggers or parsers, can be trained. In recent years, there have been increasing efforts to apply NLP methods to a much broader swathe of the worlds languages. In many cases this involves bootstrapping the learning process with enriched or partially enriched resources. One promising line of research involves the use of Interlinear Glossed Text (IGT), a very common form of annotated data used in the field of linguistics. Although IGT is generally very richly annotated, and can be enriched even further (e.g., through structural projection), much of the content is not easily consumable by machines since it remains "trapped" in linguistic scholarly documents and in human readable form. In this paper, we introduce several tools that make IGT more accessible and consumable by NLP researchers.

In this paper, we describe the expansion of the ODIN resource, a database containing many thousands of instances of Interlinear Glossed Text (IGT) for over a thousand languages. A database containing a large number of instances of IGT, which are effectively richly annotated and heuristically aligned bitexts, provides a unique resource for bootstrapping NLP tools for resource-poor languages. To make the data in ODIN more readily consumable by tool developers and NLP researchers, we propose a new XML format for IGT, called XigtWe call the updated release ODIN-II.

We present a case study of the methodology of using information extracted from interlinear glossed text (IGT) to create of actual working HPSG grammar fragments using the Grammar Matrix focusing on one language: Chintang.Though the results are barely measurable in terms of coverage over running text, they nonetheless provide a proof of concept.Our experience report reflects on the ways in which this task is non-trivial and on mismatches between the assumptions of the methodology and the realities of IGT as produced in a large-scale field project.

We propose to bring together two kinds of linguistic resources—interlinear glossed text (IGT) and a language-independent precision grammar resource—to automatically create precision grammars in the context of language documentation. This paper takes the first steps in that direction by extracting major-constituent word order and case system properties from IGT for a diverse sample of languages.

Interlinear glossed text (IGT, the familiar three-line format of linguistic examples) can be an extremely rich source of linguistic information, when linguists follow best practices in creating it (e.g., the Leipzig glossing rules, Comrie et al. 2003). The ODIN project (http://www.csufresno.edu/odin; Lewis 2006) recognized the value of IGT data as a reusable data type and has created a searchable IGT database. This paper represents early efforts in a project to combine aggregations of IGT with a second source of linguistic knowledge to automatically produce implemented formal grammars. The second source of linguistic knowledge is the LinGO Grammar Matrix customization system (Bender et al. 2010). The Grammar Matrix is a multilingual grammar engineering project which includes a cross-linguistic core HPSG (Pollard and Sag 1994) grammar and a set of analyses for cross-linguistically variable phenomena which can be selected via a web-based questionnaire. As an initial pilot study, we focus on verb morphology (including morphotactics and the morphosyntactic effects of affixes) and we begin with a best-case scenario: For our IGT, we use the complete paradigm for the French verb faire (‘to do/make’) provided by Olivier Bonami (pc), including 15,658 phonologically transcribed, morphologically segmented and glossed verb forms.