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Found 27 papers, 5 papers with code
An Unsupervised Query Rewriting Approach Using N-gram Co-occurrence Statistics to Find Similar Phrases in Large Text Corpora
We present our work towards developing a system that should find, in a large text corpus, contiguous phrases expressing similar meaning as a query phrase of arbitrary length.
Multilingual is not enough: BERT for Finnish
Deep learning-based language models pretrained on large unannotated text corpora have been demonstrated to allow efficient transfer learning for natural language processing, with recent approaches such as the transformer-based BERT model advancing the state of the art across a variety of tasks.
Is Multilingual BERT Fluent in Language Generation?
The multilingual BERT model is trained on 104 languages and meant to serve as a universal language model and tool for encoding sentences.
Template-free Data-to-Text Generation of Finnish Sports News
News articles such as sports game reports are often thought to closely follow the underlying game statistics, but in practice they contain a notable amount of background knowledge, interpretation, insight into the game, and quotes that are not present in the official statistics.
Leveraging Text Repetitions and Denoising Autoencoders in OCR Post-correction
A common approach for improving OCR quality is a post-processing step based on models correcting misdetected characters and tokens.
Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks
We evaluate our lemmatizer on 52 different languages and 76 different treebanks, showing that our system outperforms all latest baseline systems.
Evaluation of a Prototype System that Automatically Assigns Subject Headings to Nursing Narratives Using Recurrent Neural Network
Our aim is to allow nurses to write in a narrative manner without having to plan and structure the text with respect to sections and subject headings, instead the system should assist with the assignment of subject headings and restructuring afterwards.
Turku Neural Parser Pipeline: An End-to-End System for the CoNLL 2018 Shared Task
In this paper we describe the TurkuNLP entry at the CoNLL 2018 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies.
Ranked #5 on
Dependency Parsing
on Universal Dependencies
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing
Using this system, our machine learning model can be easily applied to a large set of corpora from e. g. the BioNLP, DDI Extraction and BioCreative shared tasks.
End-to-End System for Bacteria Habitat Extraction
The official evaluation shows that the joint performance of our entity detection and relation extraction models outperforms the winning team of the Shared Task by 19pp on F1-score, establishing a new top score for the task.
Detecting mentions of pain and acute confusion in Finnish clinical text
We study and compare two different approaches to the task of automatic assignment of predefined classes to clinical free-text narratives.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
