pwc-archive/papers-with-abstracts huggingface dataset:
A Multi-task Model for Multilingual Trigger Detection and Classification
Authors: Sovan Kumar Sahoo, Saumajit Saha, Asif Ekbal, Pushpak Bhattacharyya
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Classification, Multi-Task Learning

In this paper we present a deep multi-task learning framework for multilingual event and argument trigger detection and classification. In our current work, we identify detection and classification of both event and argument triggers as related tasks and follow a multi-tasking approach to solve them simultaneously in contrast to the previous works where these tasks were solved separately or learning some of the above mentioned tasks jointly. We evaluate the proposed approach with multiple low-resource Indian languages. As there were no datasets available for the Indian languages, we have annotated disaster related news data crawled from the online news portal for different low-resource Indian languages for our experiments. Our empirical evaluation shows that multi-task model performs better than the single task model, and classification helps in trigger detection and vice-versa.
Converting Sentiment Annotated Data to Emotion Annotated Data
Authors: Manasi Kulkarni, Pushpak Bhattacharyya
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Emotion Classification, Emotion Recognition, Sentence

Existing supervised solutions for emotion classification demand large amount of emotion annotated data. Such resources may not be available for many languages. However, it is common to have sentiment annotated data available in these languages. The sentiment information (+1 or -1) is useful to segregate between positive emotions or negative emotions. In this paper, we propose an unsupervised approach for emotion recognition by taking advantage of the sentiment information. Given a sentence and its sentiment information, recognize the best possible emotion for it. For every sentence, the semantic relatedness between the words from sentence and a set of emotion-specific words is calculated using cosine similarity. An emotion vector representing the emotion score for each emotion category of Ekman’s model, is created. It is further improved with the dependency relations and the best possible emotion is predicted. The results show the significant improvement in f-score values for text with sentiment information as input over our baseline as text without sentiment information. We report the weighted f-score on three different datasets with the Ekman’s emotion model. This supports that by leveraging the sentiment value, better emotion annotated data can be created.
Towards measuring lexical complexity in Malayalam
Authors: Richard Shallam, Ashwini Vaidya
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12

This paper proposes a metric to quantify lexical complexity in Malayalam. The met- ric utilizes word frequency, orthography and morphology as the three factors affect- ing visual word recognition in Malayalam. Malayalam differs from other Indian lan- guages due to its agglutinative morphology and orthography, which are incorporated into our model. The predictions made by our model are then evaluated against reac- tion times in a lexical decision task. We find that reaction times are predicted by frequency, morphological complexity and script complexity. We also explore the interactions between morphological com- plexity with frequency and script in our results. To the best of our knowledge, this is the first study on lexical complexity in Malayalam.
Kunji : A Resource Management System for Higher Productivity in Computer Aided Translation Tools
Authors: Priyank Gupta, Manish Shrivastava, Dipti Misra Sharma, Rashid Ahmad
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Machine Translation, Management, NER, Translation

Complex NLP applications, such as machine translation systems, utilize various kinds of resources namely lexical, multiword, domain dictionaries, maps and rules etc. Similarly, translators working on Computer Aided Translation workbenches, also require help from various kinds of resources - glossaries, terminologies, concordances and translation memory in the workbenches in order to increase their productivity. Additionally, translators have to look away from the workbenches for linguistic resources like Named Entities, Multiwords, lexical and lexeme dictionaries in order to get help, as the available resources like concordances, terminologies and glossaries are often not enough. In this paper we present Kunji, a resource management system for translation workbenches and MT modules. This system can be easily integrated in translation workbenches and can also be used as a management tool for resources for MT systems. The described resource management system has been integrated in a translation workbench Transzaar. We also study the impact of providing this resource management system along with linguistic resources on the productivity of translators for English-Hindi language pair. When the linguistic resources like lexeme, NER and MWE dictionaries were made available to translators in addition to their regular translation memories, concordances and terminologies, their productivity increased by 15.61%.
Identification of Synthetic Sentence in Bengali News using Hybrid Approach
Authors: Soma Das, Sanjay Chatterji
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Articles, Sentence

Often sentences of correct news are either made biased towards a particular person or a group of persons or parties or maybe distorted to add some sentiment or importance in it. Engaged readers often are not able to extract the inherent meaning of such synthetic sentences. In Bengali, the news contents of the synthetic sentences are presented in such a rich way that it usually becomes difficult to identify the synthetic part of it. We have used machine learning algorithms to classify Bengali news sentences into synthetic and legitimate and then used some rule-based postprocessing on each of these models. Finally, we have developed a voting based combination of these models to build a hybrid model for Bengali synthetic sentence identification. This is a new task and therefore we could not compare it with any existing work in the field. Identification of such types of sentences may be used to improve the performance of identifying fake news and satire news. Thus, identifying molecular level biasness in news articles.
An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data
Authors: Ashraf Kamal, Muhammad Abulaish
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Deep Learning, Marketing, Sarcasm Detection

Self-deprecating sarcasm is a special category of sarcasm, which is nowadays popular and useful for many real-life applications, such as brand endorsement, product campaign, digital marketing, and advertisement. The self-deprecating style of campaign and marketing strategy is mainly adopted to excel brand endorsement and product sales value. In this paper, we propose an LSTM-based deep learning approach for detecting self-deprecating sarcasm in textual data. To the best of our knowledge, there is no prior work related to self-deprecating sarcasm detection using deep learning techniques. Starting with a filtering step to identify self-referential tweets, the proposed approach adopts a deep learning model using LSTM for detecting self-deprecating sarcasm. The proposed approach is evaluated over three Twitter datasets and performs significantly better in terms of precision, recall, and f-score.
DRCoVe: An Augmented Word Representation Approach using Distributional and Relational Context
Authors: Md. Aslam Parwez, Muhammad Abulaish, Mohd Fazil
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Word Similarity

Word representation using the distributional information of words from a sizeable corpus is considered efficacious in many natural language processing and text mining applications. However, distributional representation of a word is unable to capture distant relational knowledge, representing the relational semantics. In this paper, we propose a novel word representation approach using distributional and relational contexts, DRCoVe, which augments the distributional representation of a word using the relational semantics extracted as syntactic and semantic association among entities from the underlying corpus. Unlike existing approaches that use external knowledge bases representing the relational semantics for enhanced word representation, DRCoVe uses typed dependencies (aka syntactic dependencies) to extract relational knowledge from the underlying corpus. The proposed approach is applied over a biomedical text corpus to learn word representation and compared with GloVe, which is one of the most popular word embedding approaches. The evaluation results on various benchmark datasets for word similarity and word categorization tasks demonstrate the effectiveness of DRCoVe over the GloVe.
Samajh-Boojh: A Reading Comprehension system in Hindi
Authors: Shalaka Vaidya, Hiranmai Sri Adibhatla, Radhika Mamidi
Date: 2019-12-01 00:00:00
Proceeding: ICON 2019 12
Tasks: Reading Comprehension, Sentence

This paper presents a novel approach designed to answer questions on a reading comprehension passage. It is an end-to-end system which first focuses on comprehending the given passage wherein it converts unstructured passage into a structured data and later proceeds to answer the questions related to the passage using solely the aforementioned structured data. To the best of our knowledge, the proposed design is first of its kind which accounts for entire process of comprehending the passage and then answering the questions associated with the passage. The comprehension stage converts the passage into a Discourse Collection that comprises of the relation shared amongst logical sentences in given passage along with the key characteristics of each sentence. This design has its applications in academic domain , query comprehension in speech systems among others.
Adverbs in Sanskrit Wordnet
Authors: Tanuja Ajotikar, Malhar Kulkarni
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1

The wordnet contains part-of-speech categories such as noun, verb, adjective and adverb. In Sanskrit, there is no formal distinction among nouns, adjectives and adverbs. This poses the question, is an adverb a separate category in Sanskrit? If not, then how do we accommodate it in a lexical resource? To investigate the issue, we attempt to study the complex nature of adverbs in Sanskrit and the policies adopted by Sanskrit lexicographers that would guide us in storing them in the Sanskrit wordnet.
Word Sense Disambiguation in Monolingual Dictionaries for Building Russian WordNet
Authors: Daniil Alexeyevsky, Anastasiya V. Temchenko
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1
Tasks: Word Sense Disambiguation

Russian Language is currently poorly supported with WordNet-like resources. One of the new efforts for building Russian WordNet involves mining the monolingual dictionaries. While most steps of the building process are straightforward, word sense disambiguation (WSD) is a source of problems. Due to limited word context specific WSD mechanism is required for each kind of relations mined. This paper describes the WSD method used for mining hypernym relations. First part of the paper explains the main reasons for choosing monolingual dictionaries as the primary source of information for Russian language WordNet and states some problems faced during the information extraction. The second part defines algorithm used to extract hyponym-hypernym pair. The third part describes the algorithm used for WSD.
Playing Alias - efficiency for wordnet(s)
Authors: Sven Aller, Heili Orav, Kadri Vare, Sirli Zupping
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1

This paper describes an electronic variant of popular word game Alias where people have to guess words according to their associations via synonyms, opposites, hyperonyms etc. Lexical data comes from the Estonian Wordnet. The computer game Alias which draws information from Estonian Wordnet is useful at least for two reasons: it creates an opportunity to learn language through play, and it helps to evaluate and improve the quality of Estonian Wordnet.
Detecting Most Frequent Sense using Word Embeddings and BabelNet
Authors: Harpreet Singh Arora, Sudha Bhingardive, Pushpak Bhattacharyya
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1
Tasks: Word Embeddings, Word Sense Disambiguation

Since the inception of the SENSEVAL evaluation exercises there has been a great deal of recent research into Word Sense Disambiguation (WSD). Over the years, various supervised, unsupervised and knowledge based WSD systems have been proposed. Beating the first sense heuristics is a challenging task for these systems. In this paper, we present our work on Most Frequent Sense (MFS) detection using Word Embeddings and BabelNet features. The semantic features from BabelNet viz., synsets, gloss, relations, etc. are used for generating sense embeddings. We compare word embedding of a word with its sense embeddings to obtain the MFS with the highest similarity. The MFS is detected for six languages viz., English, Spanish, Russian, German, French and Italian. However, this approach can be applied to any language provided that word embeddings are available for that language.
Problems and Procedures to Make Wordnet Data (Retro)Fit for a Multilingual Dictionary
Authors: Martin Benjamin
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1

The data compiled through many Wordnet projects can be a rich source of seed information for a multilingual dictionary. However, the original Princeton WordNet was not intended as a dictionary per se, and spawning other languages from it introduces inherent ambiguity that confounds precise inter-lingual linking. This paper discusses a new presentation of existing Wordnet data that displays joints (distance between predicted links) and substitution (degree of equivalence between confirmed pairs) as a two-tiered horizontal ontology. Improvements to make Wordnet data function as lexicography include term-specific English definitions where the topical synset glosses are inadequate, validation of mappings between each member of an English synset and each member of the synsets from other languages, removal of erroneous translation terms, creation of own-language definitions for the many languages where those are absent, and validation of predicted links between non-English pairs. The paper describes the current state and future directions of a system to crowdsource human review and expansion of Wordnet data, using gamification to build consensus validated, dictionary caliber data for languages now in the Global WordNet as well as new languages that do not have formal Wordnet projects of their own.
Ancient Greek WordNet Meets the Dynamic Lexicon: the Example of the Fragments of the Greek Historians
Authors: Monica Berti, Yuri Bizzoni, Federico Boschetti, Gregory R. Crane, Riccardo Del Gratta, Tariq Yousef
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1

The Ancient Greek WordNet (AGWN) and the Dynamic Lexicon (DL) are multilingual resources to study the lexicon of Ancient Greek texts and their translations. Both AGWN and DL are works in progress that need accuracy improvement and manual validation. After a detailed description of the current state of each work, this paper illustrates a methodology to cross AGWN and DL data, in order to mutually score the items of each resource according to the evidence provided by the other resource. The training data is based on the corpus of the Digital Fragmenta Historicorum Graecorum (DFHG), which includes ancient Greek texts with Latin translations.
IndoWordNet::Similarity- Computing Semantic Similarity and Relatedness using IndoWordNet
Authors: Sudha Bhingardive, Hanumant Redkar, Prateek Sappadla, Dhirendra Singh, Pushpak Bhattacharyya
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1
Tasks: Semantic Similarity, Semantic Textual Similarity

Semantic similarity and relatedness measures play an important role in natural language processing applications. In this paper, we present the IndoWordNet::Similarity tool and interface, designed for computing the semantic similarity and relatedness between two words in IndoWordNet. A java based tool and a web interface have been developed to compute this semantic similarity and relatedness. Also, Java API has been developed for this purpose. This tool, web interface and the API are made available for the research purpose.
Multilingual Sense Intersection in a Parallel Corpus with Diverse Language Families
Authors: Giulia Bonansinga, Francis Bond
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1
Tasks: Word Sense Disambiguation

Supervised methods for Word Sense Disambiguation (WSD) benefit from high-quality sense-annotated resources, which are lacking for many languages less common than English. There are, however, several multilingual parallel corpora that can be inexpensively annotated with senses through cross-lingual methods. We test the effectiveness of such an approach by attempting to disambiguate English texts through their translations in Italian, Romanian and Japanese. Specifically, we try to find the appropriate word senses for the English words by comparison with all the word senses associated to their translations. The main advantage of this approach is in that it can be applied to any parallel corpus, as long as large, high-quality inter-linked sense inventories exist for all the languages considered.
CILI: the Collaborative Interlingual Index
Authors: Francis Bond, Piek Vossen, John McCrae, Christiane Fellbaum
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1

This paper introduces the motivation for and design of the Collaborative InterLingual Index (CILI). It is designed to make possible coordination between multiple loosely coupled wordnet projects. The structure of the CILI is based on the Interlingual index first proposed in the EuroWordNet project with several pragmatic extensions: an explicit open license, definitions in English and links to wordnets in the Global Wordnet Grid.
YARN: Spinning-in-Progress
Authors: Pavel Braslavski, Dmitry Ustalov, Mikhail Mukhin, Yuri Kiselev
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1

YARN (Yet Another RussNet), a project started in 2013, aims at creating a large open WordNet-like thesaurus for Russian by means of crowdsourcing. The first stage of the project was to create noun synsets. Currently, the resource comprises 48K+ word entries and 44K+ synsets. More than 200 people have taken part in assembling synsets throughout the project. The paper describes the linguistic, technical, and organizational principles of the project, as well as the evaluation results, lessons learned, and the future plans.
Word Substitution in Short Answer Extraction: A WordNet-based Approach
Authors: Qingqing Cai, James Gung, Maochen Guan, Gerald Kurlandski, Adam Pease
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1
Tasks: Sentence, Sentence Classification

We describe the implementation of a short answer extraction system. It consists of a simple sentence selection front-end and a two phase approach to answer extraction from a sentence. In the first phase sentence classification is performed with a classifier trained with the passive aggressive algorithm utilizing the UIUC dataset and taxonomy and a feature set including word vectors. This phase outperforms the current best published results on that dataset. In the second phase, a sieve algorithm consisting of a series of increasingly general extraction rules is applied, using WordNet to find word types aligned with the UIUC classifications determined in the first phase. Some very preliminary performance metrics are presented.
An overview of Portuguese WordNets
Authors: Valeria de Paiva, Livy Real, Hugo Gonçalo Oliveira, Alexandre Rademaker, Cláudia Freitas, Alberto Simões
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1

Semantic relations between words are key to building systems that aim to understand and manipulate language. For English, the “de facto” standard for representing this kind of knowledge is Princeton’s WordNet. Here, we describe the wordnet-like resources currently available for Portuguese: their origins, methods of creation, sizes, and usage restrictions. We start tackling the problem of comparing them, but only in quantitative terms. Finally, we sketch ideas for potential collaboration between some of the projects that produce Portuguese wordnets.
Towards a WordNet based Classification of Actors in Folktales
Authors: Thierry Declerck, Tyler Klement, Antonia Kostova
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1
Tasks: Classification, Translation

In the context of a student software project we are investigating the use of WordNet for improving the automatic detection and classification of actors (or characters) mentioned in folktales. Our starting point is the book “Classification of International Folktales”, out of which we extract text segments that name the different actors involved in tales, taking advantage of patterns used by its author, Hans-Jo ̈rg Uther. We apply on those text segments functions that are implemented in the NLTK interface to WordNet in order to obtain lexical semantic information to enrich the original naming of characters proposed in the “Classification of International Folktales” and to support their translation in other languages.
Extraction and description of multi-word lexical units in plWordNet 3.0
Authors: Agnieszka Dziob, Michał Wendelberger
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1

In this paper, we present methods of extraction of multi-word lexical units (MWLUs) from large text corpora and their description in plWordNet 3.0. MWLUs are filtered from collocations of the structural type Noun+Adjective (NA).
Establishing Morpho-semantic Relations in FarsNet (a focus on derived nouns)
Authors: Nasim Fakoornia, Negar Davari Ardakani
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1
Tasks: Descriptive

This paper aims at a morpho-semantic analysis of 2461 Persian derived nouns, documented in FarsNet addressing computational codification via formulating specific morpho-semantic relations between classes of derived nouns and their bases. Considering the ultimate aim of the study, FarsNet derived nouns included 12 most productive suffixes have been analysed and as a consequence 45 morpho-semantic patterns were distinguished leading to creation of 17 morpho-semantic relations. The approach includes a close examination of beginners, grammatical category and part of speech shifts of bases undergoing the derivation process. In this research the morpho-semantic relations are considered at the word level and not at the synset level which will represent a cross-lingual validity, even if the morphological aspect of the relation is not the same in the studied languages. The resulting morpho-semantic formulations notably increase linguistic and operative competence and performance of FarsNet while is considered an achievement in Persian descriptive morphology and its codification.
Using WordNet to Build Lexical Sets for Italian Verbs
Authors: Anna Feltracco, Lorenzo Gatti, Elisabetta Jezek, Bernardo Magnini, Simone Magnolini
Date: 2016-01-01 00:00:00
Proceeding: GWC 2016 1

We present a methodology for building lexical sets for argument slots of Italian verbs. We start from an inventory of semantically typed Italian verb frames and through a mapping to WordNet we automatically annotate the sets of fillers for the argument positions in a corpus of sentences. We evaluate both a baseline algorithm and a syntax driven algorithm and show that the latter performs significantly better in terms of precision.