Search Results for author:
Found 18 papers, 10 papers with code
SRAI: Towards Standardization of Geospatial AI
Spatial Representations for Artificial Intelligence (srai) is a Python library for working with geospatial data.
Improved DeepFake Detection Using Whisper Features
With a recent influx of voice generation methods, the threat introduced by audio DeepFake (DF) is ever-increasing.
highway2vec -- representing OpenStreetMap microregions with respect to their road network characteristics
Recent years brought advancements in using neural networks for representation learning of various language or visual phenomena.
This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish
In this paper, we introduce LEPISZCZE (the Polish word for glew, the Middle English predecessor of glue), a new, comprehensive benchmark for Polish NLP with a large variety of tasks and high-quality operationalization of the benchmark.
Hex2vec -- Context-Aware Embedding H3 Hexagons with OpenStreetMap Tags
In this paper we propose the first approach to learning vector representations of OpenStreetMap regions with respect to urban functions and land-use in a micro-region grid.
gtfs2vec -- Learning GTFS Embeddings for comparing Public Transport Offer in Microregions
We selected 48 European cities and gathered their public transport timetables in the GTFS format.
Transfer Learning Approach to Bicycle-sharing Systems' Station Location Planning using OpenStreetMap Data
Bicycle-sharing systems (BSS) have become a daily reality for many citizens of larger, wealthier cities in developed regions.
Spatial Data Mining of Public Transport Incidents reported in Social Media
We successfully build an information type classifier for social media posts, detect stop names in posts, and relate them to GPS coordinates, obtaining a spatial understanding of long-term aggregated phenomena.
WER we are and WER we think we are
Natural language processing of conversational speech requires the availability of high-quality transcripts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Is the Best Better? Bayesian Statistical Model Comparison for Natural Language Processing
Recent work raises concerns about the use of standard splits to compare natural language processing models.
Avaya Conversational Intelligence: A Real-Time System for Spoken Language Understanding in Human-Human Call Center Conversations
Avaya Conversational Intelligence(ACI) is an end-to-end, cloud-based solution for real-time Spoken Language Understanding for call centers.
Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition
In this paper, we present a method for correcting automatic speech recognition (ASR) errors using a finite state transducer (FST) intent recognition framework.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
LNEMLC: Label Network Embeddings for Multi-Label Classification
Multi-label classification aims to classify instances with discrete non-exclusive labels.
Punctuation Prediction Model for Conversational Speech
The models are trained on the Fisher corpus which includes punctuation annotation.
A Network Perspective on Stratification of Multi-Label Data
We present a new approach to stratifying multi-label data for classification purposes based on the iterative stratification approach proposed by Sechidis et.
Is a Data-Driven Approach still Better than Random Choice with Naive Bayes classifiers?
In case of F1 scores and Subset Accuracy - data driven approaches were more likely to perform better than random approaches than otherwise in the worst case.
A scikit-based Python environment for performing multi-label classification
It provides native Python implementations of popular multi-label classification methods alongside a novel framework for label space partitioning and division.
How is a data-driven approach better than random choice in label space division for multi-label classification?
We show that fastgreedy and walktrap community detection methods on weighted label co-occurence graphs are 85-92% more likely to yield better F1 scores than random partitioning.
