Search Results for author: Fern

Found 36 papers, 1 papers with code

Urdu Pitch Accents and Intonation Patterns in Spontaneous Conversational Speech

no code implementations LREC 2020 Luca Rognoni, Judith Bishop, Miriam Corris, Fern, Jessica o, Rosanna Smith

An intonational inventory of Urdu for spontaneous conversational speech is determined based on the analysis of a hand-labelled data set of telephone conversations.

POS

Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications

no code implementations LREC 2020 Anita Khadka, Iv{\'a}n Cantador, Fern, Miriam ez

In this paper we address the problem of providing personalised recommendations of recent scientific publications to a particular user, and explore the use of citation knowledge to do so.

Information Retrieval

Design and Evaluation of SentiEcon: a fine-grained Economic/Financial Sentiment Lexicon from a Corpus of Business News

no code implementations LREC 2020 Antonio Moreno-Ortiz, Fern, Javier ez-Cruz, Chantal P{\'e}rez Chantal Hern{\'a}ndez

In this paper we present, describe, and evaluate SentiEcon, a large, comprehensive, domain-specific computational lexicon designed for sentiment analysis applications, for which we compiled our own corpus of online business news.

Sentence Classification Sentiment Analysis

Supervised Hypernymy Detection in Spanish through Order Embeddings

no code implementations LREC 2020 Gun Woo Lee, Mathias Etcheverry, Fern, Daniel ez Sanchez, Dina Wonsever

This paper addresses the task of supervised hypernymy detection in Spanish through an order embedding and using pretrained word vectors as input.

Transfer Learning

MSO with tests and reducts

no code implementations WS 2019 Fern, Tim o, David Woods, Carl Vogel

Tests added to Kleene algebra (by Kozen and others) are considered within Monadic Second Order logic over strings, where they are likened to statives in natural language.

CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense

1 code implementation WS 2019 Michael Chen, Mike D{'}Arcy, Alisa Liu, Fern, Jared ez, Doug Downey

To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.

Common Sense Reasoning Question Answering +1

Projecting Temporal Properties, Events and Actions

no code implementations WS 2019 Fern, Tim o

Temporal notions based on a finite set \textit{A} of properties are represented in strings, on which projections are defined that vary the granularity \textit{A}.

Sampling Informative Training Data for RNN Language Models

no code implementations ACL 2018 Fern, Jared ez, Doug Downey

We propose an unsupervised importance sampling approach to selecting training data for recurrent neural network (RNNs) language models.

Language Modelling

Comprehensive Part-Of-Speech Tag Set and SVM based POS Tagger for Sinhala

no code implementations WS 2016 Fern, S o, areka, Surangika Ranathunga, Sanath Jayasena, Gihan Dias

This paper presents a new comprehensive multi-level Part-Of-Speech tag set and a Support Vector Machine based Part-Of-Speech tagger for the Sinhala language.

POS TAG

On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter

no code implementations LREC 2014 Hassan Saif, Fern, Miriam ez, Yulan He, Harith Alani

In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods.

Classification Document Classification +2

Matching Cultural Heritage items to Wikipedia

no code implementations LREC 2012 Eneko Agirre, Ander Barrena, Oier Lopez de Lacalle, Aitor Soroa, Fern, Samuel o, Mark Stevenson

Digitised Cultural Heritage (CH) items usually have short descriptions and lack rich contextual information.

Entity Linking

Mapping WordNet synsets to Wikipedia articles

no code implementations LREC 2012 Fern, Samuel o, Mark Stevenson

Lexical knowledge bases (LKBs), such as WordNet, have been shown to be useful for a range of language processing tasks.

Information Retrieval Word Sense Disambiguation

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