Kalina Bontcheva

Other people with similar names: Katina Bontcheva


2020

pdf bib
Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour
Xingyi Song | Johnny Downs | Sumithra Velupillai | Rachel Holden | Maxim Kikoler | Kalina Bontcheva | Rina Dutta | Angus Roberts
Proceedings of the 12th Language Resources and Evaluation Conference

Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural network based classification model with a lightweight context encoder, to classify sentence level suicidal behaviour in EHRs. We show that incorporating information from sentences to left and right of the target sentence significantly improves classification accuracy. Our approach achieved the best performance when classifying suicidal behaviour in Autism Spectrum Disorder patient records. The results could have implications for suicidality research and clinical surveillance.

pdf bib
Measuring the Impact of Readability Features in Fake News Detection
Roney Santos | Gabriela Pedro | Sidney Leal | Oto Vale | Thiago Pardo | Kalina Bontcheva | Carolina Scarton
Proceedings of the 12th Language Resources and Evaluation Conference

The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, recent initiatives tried to detect fake news in different ways, ranging from language-based approaches to content-based verification. In such approaches, the choice of the features for the classification of fake and true news is one of the most important parts of the process. This paper presents a study on the impact of readability features to detect fake news for the Brazilian Portuguese language. The results show that such features are relevant to the task (achieving, alone, up to 92% classification accuracy) and may improve previous classification results.

pdf bib
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Georg Rehm | Katrin Marheinecke | Stefanie Hegele | Stelios Piperidis | Kalina Bontcheva | Jan Hajič | Khalid Choukri | Andrejs Vasiļjevs | Gerhard Backfried | Christoph Prinz | José Manuel Gómez-Pérez | Luc Meertens | Paul Lukowicz | Josef van Genabith | Andrea Lösch | Philipp Slusallek | Morten Irgens | Patrick Gatellier | Joachim Köhler | Laure Le Bars | Dimitra Anastasiou | Albina Auksoriūtė | Núria Bel | António Branco | Gerhard Budin | Walter Daelemans | Koenraad De Smedt | Radovan Garabík | Maria Gavriilidou | Dagmar Gromann | Svetla Koeva | Simon Krek | Cvetana Krstev | Krister Lindén | Bernardo Magnini | Jan Odijk | Maciej Ogrodniczuk | Eiríkur Rögnvaldsson | Mike Rosner | Bolette Pedersen | Inguna Skadiņa | Marko Tadić | Dan Tufiș | Tamás Váradi | Kadri Vider | Andy Way | François Yvon
Proceedings of the 12th Language Resources and Evaluation Conference

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

pdf bib
European Language Grid: An Overview
Georg Rehm | Maria Berger | Ela Elsholz | Stefanie Hegele | Florian Kintzel | Katrin Marheinecke | Stelios Piperidis | Miltos Deligiannis | Dimitris Galanis | Katerina Gkirtzou | Penny Labropoulou | Kalina Bontcheva | David Jones | Ian Roberts | Jan Hajič | Jana Hamrlová | Lukáš Kačena | Khalid Choukri | Victoria Arranz | Andrejs Vasiļjevs | Orians Anvari | Andis Lagzdiņš | Jūlija Meļņika | Gerhard Backfried | Erinç Dikici | Miroslav Janosik | Katja Prinz | Christoph Prinz | Severin Stampler | Dorothea Thomas-Aniola | José Manuel Gómez-Pérez | Andres Garcia Silva | Christian Berrío | Ulrich Germann | Steve Renals | Ondrej Klejch
Proceedings of the 12th Language Resources and Evaluation Conference

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented – by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 national competence centres and the European LT Council for outreach and coordination purposes.

pdf bib
Proceedings of the 1st International Workshop on Language Technology Platforms
Georg Rehm | Kalina Bontcheva | Khalid Choukri | Jan Hajič | Stelios Piperidis | Andrejs Vasiļjevs
Proceedings of the 1st International Workshop on Language Technology Platforms

pdf bib
Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
Georg Rehm | Dimitris Galanis | Penny Labropoulou | Stelios Piperidis | Martin Welß | Ricardo Usbeck | Joachim Köhler | Miltos Deligiannis | Katerina Gkirtzou | Johannes Fischer | Christian Chiarcos | Nils Feldhus | Julian Moreno-Schneider | Florian Kintzel | Elena Montiel | Víctor Rodríguez Doncel | John Philip McCrae | David Laqua | Irina Patricia Theile | Christian Dittmar | Kalina Bontcheva | Ian Roberts | Andrejs Vasiļjevs | Andis Lagzdiņš
Proceedings of the 1st International Workshop on Language Technology Platforms

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER.

2019

pdf bib
Journalist-in-the-Loop: Continuous Learning as a Service for Rumour Analysis
Twin Karmakharm | Nikolaos Aletras | Kalina Bontcheva
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Automatically identifying rumours in social media and assessing their veracity is an important task with downstream applications in journalism. A significant challenge is how to keep rumour analysis tools up-to-date as new information becomes available for particular rumours that spread in a social network. This paper presents a novel open-source web-based rumour analysis tool that can continuous learn from journalists. The system features a rumour annotation service that allows journalists to easily provide feedback for a given social media post through a web-based interface. The feedback allows the system to improve an underlying state-of-the-art neural network-based rumour classification model. The system can be easily integrated as a service into existing tools and platforms used by journalists using a REST API.

pdf bib
Team Bertha von Suttner at SemEval-2019 Task 4: Hyperpartisan News Detection using ELMo Sentence Representation Convolutional Network
Ye Jiang | Johann Petrak | Xingyi Song | Kalina Bontcheva | Diana Maynard
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the participation of team “bertha-von-suttner” in the SemEval2019 task 4 Hyperpartisan News Detection task. Our system uses sentence representations from averaged word embeddings generated from the pre-trained ELMo model with Convolutional Neural Networks and Batch Normalization for predicting hyperpartisan news. The final predictions were generated from the averaged predictions of an ensemble of models. With this architecture, our system ranked in first place, based on accuracy, the official scoring metric.

pdf bib
SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Genevieve Gorrell | Elena Kochkina | Maria Liakata | Ahmet Aker | Arkaitz Zubiaga | Kalina Bontcheva | Leon Derczynski
Proceedings of the 13th International Workshop on Semantic Evaluation

Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the danger of “fake news” has become a mainstream concern. However automated support for rumour verification remains in its infancy. It is therefore important that a shared task in this area continues to provide a focus for effort, which is likely to increase. Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour’s veracity. As in RumourEval 2017 we provided a dataset of dubious posts and ensuing conversations in social media, annotated both for stance and veracity. The social media rumours stem from a variety of breaking news stories and the dataset is expanded to include Reddit as well as new Twitter posts. There were two concrete tasks; rumour stance prediction and rumour verification, which we present in detail along with results achieved by participants. We received 22 system submissions (a 70% increase from RumourEval 2017) many of which used state-of-the-art methodology to tackle the challenges involved.

2018

pdf bib
Can Rumour Stance Alone Predict Veracity?
Sebastian Dungs | Ahmet Aker | Norbert Fuhr | Kalina Bontcheva
Proceedings of the 27th International Conference on Computational Linguistics

Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried out, none explored the effectiveness of leveraging crowd stance to determine veracity. We use stance as an additional feature to those commonly used in earlier studies. We also model the veracity of a rumour using variants of Hidden Markov Models (HMM) and the collective stance information. This paper demonstrates that HMMs that use stance and tweets’ times as the only features for modelling true and false rumours achieve F1 scores in the range of 80%, outperforming those approaches where stance is used jointly with content and user based features.

2017

pdf bib
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Leon Derczynski | Kalina Bontcheva | Maria Liakata | Rob Procter | Geraldine Wong Sak Hoi | Arkaitz Zubiaga
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Media is full of false claims. Even Oxford Dictionaries named “post-truth” as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the nature of the discourse around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics – each having their own families of claims and replies – and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.

pdf bib
Simple Open Stance Classification for Rumour Analysis
Ahmet Aker | Leon Derczynski | Kalina Bontcheva
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.

pdf bib
Automatic Summarization of Online Debates
Nattapong Sanchan | Ahmet Aker | Kalina Bontcheva
Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with RANLP 2017

Debate summarization is one of the novel and challenging research areas in automatic text summarization which has been largely unexplored. In this paper, we develop a debate summarization pipeline to summarize key topics which are discussed or argued in the two opposing sides of online debates. We view that the generation of debate summaries can be achieved by clustering, cluster labeling, and visualization. In our work, we investigate two different clustering approaches for the generation of the summaries. In the first approach, we generate the summaries by applying purely term-based clustering and cluster labeling. The second approach makes use of X-means for clustering and Mutual Information for labeling the clusters. Both approaches are driven by ontologies. We visualize the results using bar charts. We think that our results are a smooth entry for users aiming to receive the first impression about what is discussed within a debate topic containing waste number of argumentations.

2016

pdf bib
Challenges of Evaluating Sentiment Analysis Tools on Social Media
Diana Maynard | Kalina Bontcheva
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems. Firstly, these are due to differences in corpus annotation guidelines and sentiment class distribution. Secondly, different systems often make different assumptions about how to interpret certain statements, e.g. tweets with URLs. In order to study the impact of these on evaluation results, this paper focuses on tweet sentiment analysis in particular. One existing and two newly created corpora are used, and the performance of four different sentiment analysis systems is reported; we make our annotated datasets and sentiment analysis applications publicly available. We see considerable variations in results across the different corpora, which calls into question the validity of many existing annotated datasets and evaluations, and we make some observations about both the systems and the datasets as a result.

pdf bib
Monolingual Social Media Datasets for Detecting Contradiction and Entailment
Piroska Lendvai | Isabelle Augenstein | Kalina Bontcheva | Thierry Declerck
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Entailment recognition approaches are useful for application domains such as information extraction, question answering or summarisation, for which evidence from multiple sentences needs to be combined. We report on a new 3-way judgement Recognizing Textual Entailment (RTE) resource that originates in the Social Media domain, and explain our semi-automatic creation method for the special purpose of information verification, which draws on manually established rumourous claims reported during crisis events. From about 500 English tweets related to 70 unique claims we compile and evaluate 5.4k RTE pairs, while continue automatizing the workflow to generate similar-sized datasets in other languages.

pdf bib
Stance Detection with Bidirectional Conditional Encoding
Isabelle Augenstein | Tim Rocktäschel | Andreas Vlachos | Kalina Bontcheva
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
Broad Twitter Corpus: A Diverse Named Entity Recognition Resource
Leon Derczynski | Kalina Bontcheva | Ian Roberts
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

One of the main obstacles, hampering method development and comparative evaluation of named entity recognition in social media, is the lack of a sizeable, diverse, high quality annotated corpus, analogous to the CoNLL’2003 news dataset. For instance, the biggest Ritter tweet corpus is only 45,000 tokens – a mere 15% the size of CoNLL’2003. Another major shortcoming is the lack of temporal, geographic, and author diversity. This paper introduces the Broad Twitter Corpus (BTC), which is not only significantly bigger, but sampled across different regions, temporal periods, and types of Twitter users. The gold-standard named entity annotations are made by a combination of NLP experts and crowd workers, which enables us to harness crowd recall while maintaining high quality. We also measure the entity drift observed in our dataset (i.e. how entity representation varies over time), and compare to newswire. The corpus is released openly, including source text and intermediate annotations.

pdf bib
Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter
Michal Lukasik | P. K. Srijith | Duy Vu | Kalina Bontcheva | Arkaitz Zubiaga | Trevor Cohn
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

pdf bib
User profiling with geo-located posts and demographic data
Adam Poulston | Mark Stevenson | Kalina Bontcheva
Proceedings of the First Workshop on NLP and Computational Social Science

pdf bib
USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders
Isabelle Augenstein | Andreas Vlachos | Kalina Bontcheva
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

pdf bib
Modeling Tweet Arrival Times using Log-Gaussian Cox Processes
Michal Lukasik | P. K. Srijith | Trevor Cohn | Kalina Bontcheva
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

pdf bib
Classifying Tweet Level Judgements of Rumours in Social Media
Michal Lukasik | Trevor Cohn | Kalina Bontcheva
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

pdf bib
Proceedings of the International Conference Recent Advances in Natural Language Processing
Ruslan Mitkov | Galia Angelova | Kalina Bontcheva
Proceedings of the International Conference Recent Advances in Natural Language Processing

pdf bib
Efficient Named Entity Annotation through Pre-empting
Leon Derczynski | Kalina Bontcheva
Proceedings of the International Conference Recent Advances in Natural Language Processing

pdf bib
USFD: Twitter NER with Drift Compensation and Linked Data
Leon Derczynski | Isabelle Augenstein | Kalina Bontcheva
Proceedings of the Workshop on Noisy User-generated Text

pdf bib
Point Process Modelling of Rumour Dynamics in Social Media
Michal Lukasik | Trevor Cohn | Kalina Bontcheva
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

pdf bib
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Kalina Bontcheva | Jingbo Zhu
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations

pdf bib
The GATE Crowdsourcing Plugin: Crowdsourcing Annotated Corpora Made Easy
Kalina Bontcheva | Ian Roberts | Leon Derczynski | Samantha Alexander-Eames
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

pdf bib
Passive-Aggressive Sequence Labeling with Discriminative Post-Editing for Recognising Person Entities in Tweets
Leon Derczynski | Kalina Bontcheva
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

pdf bib
Proceedings of the Third Workshop on Vision and Language
Anja Belz | Darren Cosker | Frank Keller | William Smith | Kalina Bontcheva | Sien Moens | Alan Smeaton
Proceedings of the Third Workshop on Vision and Language

pdf bib
Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines
Marta Sabou | Kalina Bontcheva | Leon Derczynski | Arno Scharl
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Crowdsourcing is an emerging collaborative approach that can be used for the acquisition of annotated corpora and a wide range of other linguistic resources. Although the use of this approach is intensifying in all its key genres (paid-for crowdsourcing, games with a purpose, volunteering-based approaches), the community still lacks a set of best-practice guidelines similar to the annotation best practices for traditional, expert-based corpus acquisition. In this paper we focus on the use of crowdsourcing methods for corpus acquisition and propose a set of best practice guidelines based in our own experiences in this area and an overview of related literature. We also introduce GATE Crowd, a plugin of the GATE platform that relies on these guidelines and offers tool support for using crowdsourcing in a more principled and efficient manner.

2013

pdf bib
AnnoMarket: An Open Cloud Platform for NLP
Valentin Tablan | Kalina Bontcheva | Ian Roberts | Hamish Cunningham | Marin Dimitrov
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

pdf bib
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013
Ruslan Mitkov | Galia Angelova | Kalina Bontcheva
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

pdf bib
TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text
Kalina Bontcheva | Leon Derczynski | Adam Funk | Mark Greenwood | Diana Maynard | Niraj Aswani
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

pdf bib
Recognising and Interpreting Named Temporal Expressions
Matteo Brucato | Leon Derczynski | Hector Llorens | Kalina Bontcheva | Christian S. Jensen
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

pdf bib
Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data
Leon Derczynski | Alan Ritter | Sam Clark | Kalina Bontcheva
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

2010

pdf bib
Ontology-Based Categorization of Web Services with Machine Learning
Adam Funk | Kalina Bontcheva
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We present the problem of categorizing web services according to a shallow ontology for presentation on a specialist portal, using their WSDL and associated textual documents found by a crawler. We treat this as a text classification problem and apply first information extraction (IE) techniques (voting using keywords weight according to their context), then machine learning (ML), and finally a combined approach in which ML has priority over weighted keywords, but the latter can still make up categorizations for services for which ML does not produce enough. We evaluate the techniques (using data manually annotated through the portal, which we also use as the training data for ML) according to standard IE measures for flat categorization as well as the Balanced Distance Metric (more suitable for ontological classification) and compare them with related work in web service categorization. The ML and combined categorization results are good and the system is designed to take users' contributions through the portal's Web 2.0 features as additional training data.

2008

pdf bib
A Text-based Query Interface to OWL Ontologies
Danica Damljanovic | Valentin Tablan | Kalina Bontcheva
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Accessing structured data in the form of ontologies requires training and learning formal query languages (e.g., SeRQL or SPARQL) which poses significant difficulties for non-expert users. One of the ways to lower the learning overhead and make ontology queries more straightforward is through a Natural Language Interface (NLI). While there are existing NLIs to structured data with reasonable performance, they tend to require expensive customisation to each new domain or ontology. Additionally, they often require specific adherence to a pre-defined syntax which, in turn, means that users still have to undergo training. In this paper we present Question-based Interface to Ontologies (QuestIO) - a tool for querying ontologies using unconstrained language-based queries. QuestIO has a very simple interface, requires no user training and can be easily embedded in any system or used with any ontology or knowledge base without prior customisation.

pdf bib
Coling 2008: Companion volume: Demonstrations
Allan Ramsay | Kalina Bontcheva
Coling 2008: Companion volume: Demonstrations

2006

pdf bib
User-friendly ontology authoring using a controlled language
Valentin Tablan | Tamara Polajnar | Hamish Cunningham | Kalina Bontcheva
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In recent years, following the rapid development in the Semantic Web and Knowledge Management research, ontologies have become more in demand in Natural Language Processing. An increasing number of systems use ontologies either internally, for modelling the domain of the application, or as data structures that hold the output resulting from the work of the system, in the form of knowledge bases. While there are many ontology editing tools aimed at expert users, there are very few which are accessible to users wishing to create simple structures without delving into the intricacies of knowledge representation languages. The approach described in this paper allows users to create and edit ontologies simply by using a restricted version of the English language. The controlled language described within is based on an open vocabulary and a restricted set of grammatical constructs. Sentences written in this language unambiguously map into a number of knowledge representation formats including OWL and RDF-S to allow round-trip ontology management.

2005

pdf bib
Perceptron Learning for Chinese Word Segmentation
Yaoyong Li | Chuanjiang Miao | Kalina Bontcheva | Hamish Cunningham
Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing

pdf bib
Using Uneven Margins SVM and Perceptron for Information Extraction
Yaoyong Li | Kalina Bontcheva | Hamish Cunningham
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

2004

pdf bib
Automatic Language-Independent Induction of Gazetteer Lists
Diana Maynard | Kalina Bontcheva | Hamish Cunningham
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

pdf bib
Open-source Tools for Creation, Maintenance, and Storage of Lexical Resources for Language Generation from Ontologies
Kalina Bontcheva
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

pdf bib
Web Services Architecture for Language Resources
Angelo Dalli | Valentin Tablan | Kalina Bontcheva | Yorick Wilks | Daan Broeder | Hennie Brugman | Peter Wittenburg
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

pdf bib
Large Scale Experiments for Semantic Labeling of Noun Phrases in Raw Text
Louise Guthrie | Roberto Basili | Fabio Zanzotto | Kalina Bontcheva | Hamish Cunningham | David Guthrie | Jia Cui | Marco Cammisa | Jerry Cheng-Chieh Liu | Cassia Farria Martin | Kristiyan Haralambiev | Martin Holub | Klaus Macherey | Fredrick Jelinek
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2003

pdf bib
Experiments with geographic knowledge for information extraction
Dimitar Manov | Atanas Kiryakov | Borislav Popov | Kalina Bontcheva | Diana Maynard | Hamish Cunningham
Proceedings of the HLT-NAACL 2003 Workshop on Analysis of Geographic References

pdf bib
OLLIE: On-Line Learning for Information Extraction
Valentin Tablan | Kalina Bontcheva | Diana Maynard | Hamish Cunningham
Proceedings of the HLT-NAACL 2003 Workshop on Software Engineering and Architecture of Language Technology Systems (SEALTS)

pdf bib
Reuse and Challenges in Evaluating Language Generation Systems: Position Paper
Kalina Bontcheva
Proceedings of the EACL 2003 Workshop on Evaluation Initiatives in Natural Language Processing: are evaluation methods, metrics and resources reusable?

pdf bib
Multilingual adaptations of a reusable information extraction tool
Diana Maynard | Hamish Cunningham | Kalina Bontcheva
Demonstrations

pdf bib
Robust Generic and Query-based Summarization
Horacio Saggion | Kalina Bontcheva | Hamish Cunningham
Demonstrations

2002

pdf bib
Using GATE as an Environment for Teaching NLP
Kalina Bontcheva | Hamish Cunningham | Valentin Tablan | Diana Maynard | Oana Hamza
Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics

pdf bib
Using a text engineering framework to build an extendable and portable IE-based summarisation system
Diana Maynard | Kalina Bontcheva | Horacio Saggion | Hamish Cunningham | Oana Hamza
Proceedings of the ACL-02 Workshop on Automatic Summarization

pdf bib
GATE: an Architecture for Development of Robust HLT applications
Hamish Cunningham | Diana Maynard | Kalina Bontcheva | Valentin Tablan
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

pdf bib
Extracting Information for Automatic Indexing of Multimedia Material
Horacio Saggion | Hamish Cunningham | Diana Maynard | Kalina Bontcheva | Oana Hamza | Christian Ursu | Yorick Wilks
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

pdf bib
A Unicode-based Environment for Creation and Use of Language Resources
Valentin Tablan | Cristian Ursu | Kalina Bontcheva | Hamish Cunningham | Diana Maynard | Oana Hamza | Tony McEnery | Paul Baker | Mark Leisher
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

pdf bib
Using HLT for Acquiring, Retrieving and Publishing Knowledge in AKT
Kalina Bontcheva | Christopher Brewster | Fabio Ciravegna | Hamish Cunningham | Louise Guthrie | Robert Gaizauskas | Yorick Wilks
Proceedings of the ACL 2001 Workshop on Human Language Technology and Knowledge Management

2000

pdf bib
Software Infrastructure for Language Resources: a Taxonomy of Previous Work and a Requirements Analysis
Hamish Cunningham | Kalina Bontcheva | Valentin Tablan | Yorick Wilks
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

pdf bib
Experience using GATE for NLP R&D
Hamish Cunningham | Diana Maynard | Kalina Bontcheva | Valentin Tablan | Yorick Wilks
Proceedings of the COLING-2000 Workshop on Using Toolsets and Architectures To Build NLP Systems

pdf bib
An Experiment in Unifying Audio-Visual and Textual Infrastructures for Language Processing Research and Development
Kalina Bontcheva | Hennie Brugman | Hamish Cunningham | Albert Russel | Peter Wittenburg
Proceedings of the COLING-2000 Workshop on Using Toolsets and Architectures To Build NLP Systems

1996

pdf bib
NL Domain Explanations in Knowledge Based MAT
Galia Angelova | Kalina Bontcheva
COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics

Search
Co-authors