pwc-archive/papers-with-abstracts huggingface dataset:
Entity-Centric Information Access with Human in the Loop for the Biomedical Domain
Authors: Seid Muhie Yimam, Steffen Remus, Alex Panchenko, er, Andreas Holzinger, Chris Biemann
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Management

In this paper, we describe the concept of entity-centric information access for the biomedical domain. With entity recognition technologies approaching acceptable levels of accuracy, we put forward a paradigm of document browsing and searching where the entities of the domain and their relations are explicitly modeled to provide users the possibility of collecting exhaustive information on relations of interest. We describe three working prototypes along these lines: NEW/S/LEAK, which was developed for investigative journalists who need a quick overview of large leaked document collections; STORYFINDER, which is a personalized organizer for information found in web pages that allows adding entities as well as relations, and is capable of personalized information management; and adaptive annotation capabilities of WEBANNO, which is a general-purpose linguistic annotation tool. We will discuss future steps towards the adaptation of these tools to biomedical data, which is subject to a recently started project on biomedical knowledge acquisition. A key difference to other approaches is the centering around the user in a Human-in-the-Loop machine learning approach, where users define and extend categories and enable the system to improve via feedback and interaction.
One model per entity: using hundreds of machine learning models to recognize and normalize biomedical names in text
Authors: Victor Bellon, Raul Rodriguez-Esteban
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: BIG-bench Machine Learning, Domain Adaptation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

We explored a new approach to named entity recognition based on hundreds of machine learning models, each trained to distinguish a single entity, and showed its application to gene name identification (GNI). The rationale for our approach, which we named {``}one model per entity{''} (OMPE), was that increasing the number of models would make the learning task easier for each individual model. Our training strategy leveraged freely-available database annotations instead of manually-annotated corpora. While its performance in our proof-of-concept was disappointing, we believe that there is enough room for improvement that such approaches could reach competitive performance while eliminating the cost of creating costly training corpora.
Towards Confidence Estimation for Typed Protein-Protein Relation Extraction
Authors: Camilo Thorne, Roman Klinger
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Entity Linking, Named Entity Recognition (NER), Relation, Relation Extraction

Systems which build on top of information extraction are typically challenged to extract knowledge that, while correct, is not yet well-known. We hypothesize that a good confidence measure for relational information has the property that such interesting information is found between information extracted with very high confidence and very low confidence. We discuss confidence estimation for the domain of biomedical protein-protein relation discovery in biomedical literature. As facts reported in papers take some time to be validated and recorded in biomedical databases, such task gives rise to large quantities of unknown but potentially true candidate relations. It is thus important to rank them based on supporting evidence rather than discard them. In this paper, we discuss this task and propose different approaches for confidence estimation and a pipeline to evaluate such methods. We show that the most straight-forward approach, a combination of different confidence measures from pipeline modules seems not to work well. We discuss this negative result and pinpoint potential future research directions.
Identification of Risk Factors in Clinical Texts through Association Rules
Authors: Svetla Boytcheva, Ivelina Nikolova, Galia Angelova, Zhivko Angelov
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

We describe a method which extracts Association Rules from texts in order to recognise verbalisations of risk factors. Usually some basic vocabulary about risk factors is known but medical conditions are expressed in clinical narratives with much higher variety. We propose an approach for data-driven learning of specialised medical vocabulary which, once collected, enables early alerting of potentially affected patients. The method is illustrated by experimens with clinical records of patients with Chronic Obstructive Pulmonary Disease (COPD) and comorbidity of CORD, Diabetes Melitus and Schizophrenia. Our input data come from the Bulgarian Diabetic Register, which is built using a pseudonymised collection of outpatient records for about 500,000 diabetic patients. The generated Association Rules for CORD are analysed in the context of demographic, gender, and age information. Valuable anounts of meaningful words, signalling risk factors, are discovered with high precision and confidence.
POMELO: Medline corpus with manually annotated food-drug interactions
Authors: Thierry Hamon, Vincent Tabanou, Fleur Mougin, Natalia Grabar, Frantz Thiessard
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

When patients take more than one medication, they may be at risk of drug interactions, which means that a given drug can cause unexpected effects when taken in combination with other drugs. Similar effects may occur when drugs are taken together with some food or beverages. For instance, grapefruit has interactions with several drugs, because its active ingredients inhibit enzymes involved in the drugs metabolism and can then cause an excessive dosage of these drugs. Yet, information on food/drug interactions is poorly researched. The current research is mainly provided by the medical domain and a very tentative work is provided by computer sciences and NLP domains. One factor that motivates the research is related to the availability of the annotated corpora and the reference data. The purpose of our work is to describe the rationale and approach for creation and annotation of scientific corpus with information on food/drug interactions. This corpus contains 639 MEDLINE citations (titles and abstracts), corresponding to 5,752 sentences. It is manually annotated by two experts. The corpus is named POMELO. This annotated corpus will be made available for the research purposes.
Annotation of Clinical Narratives in Bulgarian language
Authors: Ivajlo Radev, Kiril Simov, Galia Angelova, Svetla Boytcheva
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Chunking, Dependency Parsing, Information Retrieval

In this paper we describe annotation process of clinical texts with morphosyntactic and semantic information. The corpus contains 1,300 discharge letters in Bulgarian language for patients with Endocrinology and Metabolic disorders. The annotated corpus will be used as a Gold standard for information extraction evaluation of test corpus of 6,200 discharge letters. The annotation is performed within Clark system {---} an XML Based System For Corpora Development. It provides mechanism for semi-automatic annotation first running a pipeline for Bulgarian morphosyntactic annotation and a cascaded regular grammar for semantic annotation is run, then rules for cleaning of frequent errors are applied. At the end the result is manually checked. At the end we hope also to be able to adapted the morphosyntactic tagger to the domain of clinical narratives as well.
A Diachronic Corpus for Romanian (RoDia)
Authors: Ludmila Malahov, C{\u{a}}t{\u{a}}lina M{\u{a}}r{\u{a}}nduc, Alex Colesnicov, ru
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Information Retrieval, Optical Character Recognition (OCR), Question Answering

This paper describes a Romanian Dependency Treebank, built at the Al. I. Cuza University (UAIC), and a special OCR techniques used to build it. The corpus has rich morphological and syntactic annotation. There are few annotated representative corpora in Romanian, and the existent ones are mainly focused on the contemporary Romanian standard. The corpus described below is focused on the non-standard aspects of the language, the Regional and the Old Romanian. Having the intention to participate at the PROIEL project, which aligns oldest New Testaments, we annotate the first printed Romanian New Testament (Alba Iulia, 1648). We began by applying the UAIC tools for the morphological and syntactic processing of Contemporary Romanian over the book{'}s first quarter (second edition). By carefully manually correcting the result of the automated annotation (having a modest accuracy) we obtained a sub-corpus for the training of tools for the Old Romanian processing. But the first edition of the New Testament is written in Cyrillic letters. The existence of books printed in the Old Cyrillic alphabet is a common problem for Romania and The Republic of Moldova, countries where the Romanian is spoken; a problem to solve by the joint efforts of the NLP researchers in the two countries.
Tools for Building a Corpus to Study the Historical and Geographical Variation of the Romanian Language
Authors: Victoria Bobicev, C{\u{a}}t{\u{a}}lina M{\u{a}}r{\u{a}}nduc, Cenel Augusto Perez
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

Contemporary standard language corpora are ideal for NLP. There are few morphologically and syntactically annotated corpora for Romanian, and those existing or in progress only deal with the Contemporary Romanian standard. However, the necessity to study the dynamics of natural languages gave rise to balanced corpora, containing non-standard texts. In this paper, we describe the creation of tools for processing non-standard Romanian to build a big balanced corpus. We want to preserve in annotated form as many early stages of language as possible. We have already built a corpus in Old Romanian. We also intend to include the South-Danube dialects, remote to the standard language, along with regional forms closer to the standard. We try to preserve data about endangered idioms such as Aromanian, Meglenoromanian and Istroromanian dialects, and calculate the distance between different regional variants, including the language spoken in the Republic of Moldova. This distance, as well as the mutual understanding between the speakers, is the correct criterion for the classification of idioms as different languages, or as dialects, or as regional variants close to the standard.
Multilingual Ontologies for the Representation and Processing of Folktales
Authors: Thierry Declerck, Anastasija Aman, Martin Banzer, Dominik Mach{\'a}{\v{c}}ek, Lisa Sch{\"a}fer, Natalia Skachkova
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: General Classification, Text Generation

We describe work done in the field of folkloristics and consisting in creating ontologies based on well-established studies proposed by {``}classical{''} folklorists. This work is supporting the availability of a huge amount of digital and structured knowledge on folktales to digital humanists. The ontological encoding of past and current motif-indexation and classification systems for folktales was in the first step limited to English language data. This led us to focus on making those newly generated formal knowledge sources available in a few more languages, like German, Russian and Bulgarian. We stress the importance of achieving this multilingual extension of our ontologies at a larger scale, in order for example to support the automated analysis and classification of such narratives in a large variety of languages, as those are getting more and more accessible on the Web.
On the annotation of vague expressions: a case study on Romanian historical texts
Authors: Anca Dinu, Walther von Hahn, Cristina Vertan
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

Current approaches in Digital .Humanities tend to ignore a central as-pect of any hermeneutic introspection: the intrinsic vagueness of analyzed texts. Especially when dealing with his-torical documents neglecting vague-ness has important implications on the interpretation of the results. In this pa-per we present current limitation of an-notation approaches and describe a current methodology for annotating vagueness for historical Romanian texts.
Language Technologies in Teaching Bugarian at Primary and Secondary School Level: the NBU Platform of Language Teaching (PLT)
Authors: Maria Stambolieva, Valentina Ivanova, Mariana Raykova, Milka Hadjikoteva, Mariya Neykova
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

The NBU Language Teaching Platform (PLT) was initially designed for teaching foreign languages for specific purposes; at a second stage, some of its functionalities were extended to answer the needs of teaching general foreign language. New functionalities have now been created for the purpose of providing e-support for Bulgarian language and literature teaching at primary and secondary school level. The article presents the general structure of the platform and the functionalities specifically developed to match the standards and expected results set by the Ministry of Education. The E-platform integrates: 1/ an environment for creating, organizing and maintaining electronic text archives, for extracting text corpora and aligning corpora; 2/ a linguistic database; 3/ a concordancer; 4/ a set of modules for the generation and editing of practice exercises for each text or corpus; 5/ functionalities for export from the platform and import to other educational platforms. For Moodle, modules were created for test generation, performance assessment and feedback. The PLT allows centralized presentation of abundant teaching content, control of the educational process, fast and reliable feedback on performance.
Natural Language Processing in Political Campaigns
Authors: Cristina Moise
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Opinion Mining, Sentiment Analysis

This paper overviews the Majoritas ecosystem, providing a complete overview of political campaigns assessment aimed to assist politicians and their staff in delivering consistent and personalized message within social media.
Compound Type Identification in Sanskrit: What Roles do the Corpus and Grammar Play?
Authors: Amrith Krishna, Pavankumar Satuluri, Shubham Sharma, Apurv Kumar, Pawan Goyal
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Classification, General Classification, Machine Translation, Question Answering

We propose a classification framework for semantic type identification of compounds in Sanskrit. We broadly classify the compounds into four different classes namely, \textit{Avyay{\=\i}bh{\=a}va}, \textit{Tatpuruṣa}, \textit{Bahuvr{\=\i}hi} and \textit{Dvandva}. Our classification is based on the traditional classification system followed by the ancient grammar treatise \textit{Adṣṭ{\=a}dhy{\=a}y{\=\i}}, proposed by P{\=a}ṇini 25 centuries back. We construct an elaborate features space for our system by combining conditional rules from the grammar \textit{Adṣṭ{\=a}dhy{\=a}y{\=\i}}, semantic relations between the compound components from a lexical database \textit{Amarakoṣa} and linguistic structures from the data using Adaptor Grammars. Our in-depth analysis of the feature space highlight inadequacy of \textit{Adṣṭ{\=a}dhy{\=a}y{\=\i}}, a generative grammar, in classifying the data samples. Our experimental results validate the effectiveness of using lexical databases as suggested by Amba Kulkarni and Anil Kumar, and put forward a new research direction by introducing linguistic patterns obtained from Adaptor grammars for effective identification of compound type. We utilise an ensemble based approach, specifically designed for handling skewed datasets and we {\%}and Experimenting with various classification methods, we achieve an overall accuracy of 0.77 using random forest classifiers.
Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary
Authors: Ye Kyaw Thu, Win Pa Pa, Yoshinori Sagisaka, Naoto Iwahashi
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Active Learning, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Grapheme-to-Phoneme Conversion, Machine Translation, speech-recognition, Speech Recognition, text-to-speech, Text to Speech, Translation

Grapheme-to-Phoneme (G2P) conversion is the task of predicting the pronunciation of a word given its graphemic or written form. It is a highly important part of both automatic speech recognition (ASR) and text-to-speech (TTS) systems. In this paper, we evaluate seven G2P conversion approaches: Adaptive Regularization of Weight Vectors (AROW) based structured learning (S-AROW), Conditional Random Field (CRF), Joint-sequence models (JSM), phrase-based statistical machine translation (PBSMT), Recurrent Neural Network (RNN), Support Vector Machine (SVM) based point-wise classification, Weighted Finite-state Transducers (WFST) on a manually tagged Myanmar phoneme dictionary. The G2P bootstrapping experimental results were measured with both automatic phoneme error rate (PER) calculation and also manual checking in terms of voiced/unvoiced, tones, consonant and vowel errors. The result shows that CRF, PBSMT and WFST approaches are the best performing methods for G2P conversion on Myanmar language.
Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media
Authors: Mourad Gridach
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Feature Engineering, Information Retrieval, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Opinion Mining, Question Answering, Text Clustering

Named Entity Recognition (NER) is the task of classifying or labelling atomic elements in the text into categories such as Person, Location or Organisation. For Arabic language, recognizing named entities is a challenging task because of the complexity and the unique characteristics of this language. In addition, most of the previous work focuses on Modern Standard Arabic (MSA), however, recognizing named entities in social media is becoming more interesting these days. Dialectal Arabic (DA) and MSA are both used in social media, which is deemed as another challenging task. Most state-of-the-art Arabic NER systems count heavily on handcrafted engineering features and lexicons which is time consuming. In this paper, we introduce a novel neural network architecture which benefits both from character- and word-level representations automatically, by using combination of bidirectional LSTM and Conditional Random Field (CRF), eliminating the need for most feature engineering. Moreover, our model relies on unsupervised word representations learned from unannotated corpora. Experimental results demonstrate that our model achieves state-of-the-art performance on publicly available benchmark for Arabic NER for social media and surpassing the previous system by a large margin.
Development of a Bengali parser by cross-lingual transfer from Hindi
Authors: Ayan Das, Agnivo Saha, Sudeshna Sarkar
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Chunking, Cross-Lingual Transfer, Sentence

In recent years there has been a lot of interest in cross-lingual parsing for developing treebanks for languages with small or no annotated treebanks. In this paper, we explore the development of a cross-lingual transfer parser from Hindi to Bengali using a Hindi parser and a Hindi-Bengali parallel corpus. A parser is trained and applied to the Hindi sentences of the parallel corpus and the parse trees are projected to construct probable parse trees of the corresponding Bengali sentences. Only about 14{\%} of these trees are complete (transferred trees contain all the target sentence words) and they are used to construct a Bengali parser. We relax the criteria of completeness to consider well-formed trees (43{\%} of the trees) leading to an improvement. We note that the words often do not have a one-to-one mapping in the two languages but considering sentences at the chunk-level results in better correspondence between the two languages. Based on this we present a method to use chunking as a preprocessing step and do the transfer on the chunk trees. We find that about 72{\%} of the projected parse trees of Bengali are now well-formed. The resultant parser achieves significant improvement in both Unlabeled Attachment Score (UAS) as well as Labeled Attachment Score (LAS) over the baseline word-level transferred parser.
Sinhala Short Sentence Similarity Calculation using Corpus-Based and Knowledge-Based Similarity Measures
Authors: Jcs Kadupitiya, Surangika Ranathunga, Gihan Dias
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Similarity, Word Sense Disambiguation

Currently, corpus based-similarity, string-based similarity, and knowledge-based similarity techniques are used to compare short phrases. However, no work has been conducted on the similarity of phrases in Sinhala language. In this paper, we present a hybrid methodology to compute the similarity between two Sinhala sentences using a Semantic Similarity Measurement technique (corpus-based similarity measurement plus knowledge-based similarity measurement) that makes use of word order information. Since Sinhala WordNet is still under construction, we used lexical resources in performing this semantic similarity calculation. Evaluation using 4000 sentence pairs yielded an average MSE of 0.145 and a Pearson correla-tion factor of 0.832.
Enriching Source for English-to-Urdu Machine Translation
Authors: Bushra Jawaid, Amir Kamran, Ond{\v{r}}ej Bojar
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Translation

This paper focuses on the generation of case markers for free word order languages that use case markers as phrasal clitics for marking the relationship between the dependent-noun and its head. The generation of such clitics becomes essential task especially when translating from fixed word order languages where syntactic relations are identified by the positions of the dependent-nouns. To address the problem of missing markers on source-side, artificial markers are added in source to improve alignments with its target counterparts. Up to 1 BLEU point increase is observed over the baseline on different test sets for English-to-Urdu.