FinnFN 1.0: The Finnish frame semantic database

The article describes the process of creating a Finnish language FrameNet or FinnFN, based on the original English language FrameNet hosted at the International Computer Science Institute in Berkeley, California. We outline the goals and results relating to the FinnFN project and especially to the creation of the FinnFrame corpus. The main aim of the project was to test the universal applicability of frame semantics by annotating real Finnish using the same frames and annotation conventions as in the original Berkeley FrameNet project. From Finnish newspaper corpora, 40,721 sentences were automatically retrieved and manually annotated as example sentences evoking certain frames. This became the FinnFrame corpus. Applying the Berkeley FrameNet annotation conventions to the Finnish language required some modifications due to Finnish morphology, and a convention for annotating individual morphemes within words was introduced for phenomena such as compounding, comparatives and case endings. Various questions about cultural salience across the two languages arose during the project, but problematic situations occurred only in a few examples, which we also discuss in the article. The article shows that, barring a few minor instances, the universality hypothesis of frames is largely confirmed for languages as different as Finnish and English.Peer reviewe

FinnTransFrame : translating frames in the FinnFrameNet project

The article details the formational process of the FinnTransFrame corpus, a part of the FinnFrameNet project. In addition to a large annotated frame semantic corpus of natural language examples, the project created a separate corpus of examples translated from English to Finnish. The research question when creating the FinnTransFrame corpus was to see to what extent the various frames of the original Berkeley FrameNet transfer into Finnish in translated examples, i.e. what are the main problems and how can they be categorized? A variety of Berkeley FrameNet examples were chosen from different frames and then translated by professionals. The FinnFrameNet annotation team checked all the examples and their translations to see if the frames remained intact in translation. Problematic examples were tagged according to the type of the encountered problem, with the main focus on the type of fine-grained mismatches of meaning that caused frame changes even when the translation was the best possible one. The frame-loss amounted to 4.2% of the 88,209 relevant example sentences. Filtering out sentences with other types of problems, we found that 88.1% of all the frame instances still translated into Finnish with their frame intact. In addition, the article analyzes the error types in the problematic frames.Peer reviewe

MaskParse@Deskin at SemEval-2019 Task 1: Cross-lingual UCCA Semantic Parsing using Recursive Masked Sequence Tagging

International audienceThis paper describes our recursive system for SemEval-2019 \textit{ Task 1: Cross-lingual Semantic Parsing with UCCA}. Each recursive step consists of two parts. We first perform semantic parsing using a sequence tagger to estimate the probabilities of the UCCA categories in the sentence. Then, we apply a decoding policy which interprets these probabilities and builds the graph nodes. Parsing is done recursively, we perform a first inference on the sentence to extract the main scenes and links and then we recursively apply our model on the sentence using a masking feature that reflects the decisions made in previous steps. Process continues until the terminal nodes are reached. We choose a standard neural tagger and we focused on our recursive parsing strategy and on the cross lingual transfer problem to develop a robust model for the French language, using only few training samples

Enhancing a role and reference grammar approach to English motion constructions in a Natural Language Processing environment

This paper puts forward a finer-grained computational treatment of the English caused-motion construction (e.g. He kicked the ball into the net) within a knowledge base for natural language processing systems called FunGramKB. This computational project is largely based on Role and Reference Grammar (RRG), which is a functional projectionist theory of language. We argue that the RRG-based characterization of the caused-motion construction in FunGramKB is insufficient to account for the semantic and syntactic complexity of realizations such as He walked the dog to the park, I will show you out, or Mac flew Continental to Bush International Airport. Thus, drawing on insights from Constructions Grammars, three minimally distinct transitive motion sub-constructions are formalized within FunGramKB. It is through the inclusion of additional constructional schemas that the machine will be able to capture the various ways in which verbs and constructions interact to yield different input textsEste artículo presenta un tratamiento computacional más fino de la construcción de movimiento causado en inglés (por ejemplo, He kicked the ball into the net, “metió de una patada la pelota en la red”) en una base de conocimientos para sistemas de Procesamiento de Lenguaje Natural llamada FunGramKB. Este proyecto computacional se basa en gran medida en la Gramática del Papel y la Referencia (RRG), que es una teoría funcionalista del lenguaje. Argumentamos que la caracterización basada en la RRG de la construcción de movimiento causado en FunGramKB es insuficiente para explicar la complejidad semántica y sintáctica de realizaciones tales como He walked the dog to the park, I will show you out, or Mac flew Continental to Bush International Airport , “Sacó a pasear al perro al parque, Te enseño la salida, Mac voló Continental al Aeropuerto Internacional Bush”. Así, basándose en las propuestas de las Gramáticas de Construcciones, se formalizan dentro de FunGramKB tres sub-construcciones de movimiento transitivas ligeramente distintas. A través de la de esquemas constructivos adicionales la máquina será capaz de dar cuenta de las diversas formas en que interactúan los verbos y las construcciones para producir diferentes textos de entradaThe research projects on which this paper is based have received financial support from the Spanish Ministry of Economy and Competitiveness, grants no. FFI2013- 43593-P and FFI2014-53788-C3-1-