FrameNet automatic analysis : a study on a French corpus of encyclopedic texts

Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning

This paper presents a new semantic frame parsing model, based on Berkeley FrameNet, adapted to process spoken documents in order to perform information extraction from broadcast contents. Building upon previous work that had shown the effectiveness of adversarial learning for domain generalization in the context of semantic parsing of encyclopedic written documents, we propose to extend this approach to elocutionary style generalization. The underlying question throughout this study is whether adversarial learning can be used to combine data from different sources and train models on a higher …

OLDI 种子语料库的法语版本

A French Version of the OLDI Seed Corpus

本文介绍了OLDI种子语料库的首个法语分区，详述了其创建过程与翻译挑战，并定位为促进法国地区语言平行语料收集的关键资源。

Semantic Frame Parsing for Information Extraction : the CALOR corpus

This paper presents a publicly available corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism. The main difference in our approach compared to previous works on semantic parsing with FrameNet is that we are not interested here in full text parsing but rather on partial parsing. The goal is to select from the FrameNet resources the minimal set of frames that are going to be useful for the applicative framework targeted, in our case Information Extraction from encyclopedic documents. Such an approach leverages the manual annotation of larger corpora …

多语言框架网络语料库

The Multilingual FrameNet Corpus

本研究构建了多语言框架网络语料库（mFNC），在其上训练不同架构的模型，在多语言和跨语言场景中超越现有最先进框架语义解析器，凸显多语言训练数据的重要性，相关资源公开可用。

Coarse-grained Cross-lingual Alignment of Comparable Texts with Topic Models and Encyclopedic Knowledge

We present a method for coarse-grained cross-lingual alignment of comparable texts: segments consisting of contiguous paragraphs that discuss the same theme (e.g. history, economy) are aligned based on induced multilingual topics. The method combines three ideas: a two-level LDA model that filters out words that do not convey themes, an HMM that models the ordering of themes in the collection of documents, and language-independent concept annotations to serve as a cross-language bridge and to strengthen the connection between paragraphs in the same segment through concept relations. The method…

Sources of Complexity in Semantic Frame Parsing for Information Extraction

This paper describes a Semantic Frame parsing System based on sequence labeling methods, precisely BiLSTM models with highway connections, for performing information extraction on a corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism. The approach proposed in this study relies on an integrated sequence labeling model which jointly optimizes frame identification and semantic role segmentation and identification. The purpose of this study is to analyze the task complexity, to highlight the factors that make Semantic Frame parsing a difficult task an…