pwc-archive/papers-with-abstracts huggingface dataset:
A Finite-State Morphological Analyser for Sindhi
Authors: Raveesh Motlani, Francis Tyers, Dipti Sharma
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Information Retrieval, LEMMA, Machine Translation, Morphological Analysis, Part-Of-Speech Tagging, Retrieval, Translation

Morphological analysis is a fundamental task in natural-language processing, which is used in other NLP applications such as part-of-speech tagging, syntactic parsing, information retrieval, machine translation, etc. In this paper, we present our work on the development of free/open-source finite-state morphological analyser for Sindhi. We have used Apertium{'}s lttoolbox as our finite-state toolkit to implement the transducer. The system is developed using a paradigm-based approach, wherein a paradigm defines all the word forms and their morphological features for a given stem (lemma). We have evaluated our system on the Sindhi Wikipedia corpus and achieved a reasonable coverage of 81{\%} and a precision of over 97{\%}.
Deriving Morphological Analyzers from Example Inflections
Authors: Markus Forsberg, Mans Hulden
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents a semi-automatic method to derive morphological analyzers from a limited number of example inflections suitable for languages with alphabetic writing systems. The system we present learns the inflectional behavior of morphological paradigms from examples and converts the learned paradigms into a finite-state transducer that is able to map inflected forms of previously unseen words into lemmas and corresponding morphosyntactic descriptions. We evaluate the system when provided with inflection tables for several languages collected from the Wiktionary.
Morphological Analysis of Sahidic Coptic for Automatic Glossing
Authors: Daniel Smith, Mans Hulden
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Morphological Analysis, Transliteration

We report on the implementation of a morphological analyzer for the Sahidic dialect of Coptic, a now extinct Afro-Asiatic language. The system is developed in the finite-state paradigm. The main purpose of the project is provide a method by which scholars and linguists can semi-automatically gloss extant texts written in Sahidic. Since a complete lexicon containing all attested forms in different manuscripts requires significant expertise in Coptic spanning almost 1,000 years, we have equipped the analyzer with a core lexicon and extended it with a {``}guesser{''} ability to capture out-of-vocabulary items in any inflection. We also suggest an ASCII transliteration for the language. A brief evaluation is provided.
The on-line version of Grammatical Dictionary of Polish
Authors: Marcin Woli{\'n}ski, Witold Kiera{\'s}
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present the new online edition of a dictionary of Polish inflection â€• the Grammatical Dictionary of Polish (http://sgjp.pl). The dictionary is interesting for several reasons: it is comprehensive (over 330,000 lexemes corresponding to almost 4,300,000 different textual words; 1116 handcrafted inflectional patterns), the inflection is presented in an explicit manner in the form of carefully designed tables, the user interface facilitates advanced queries by several features (lemmas, forms, applicable grammatical categories, types of inflection). Moreover, the data of the dictionary is used in morphological analysers, including our product Morfeusz (http://sgjp. pl/morfeusz). From the start, the dictionary was meant to be comfortable for the human reader as well as to be ready for use in NLP applications. In the paper we briefly discuss both aspects of the resource.
Automatically Generated Affective Norms of Abstractness, Arousal, Imageability and Valence for 350 000 German Lemmas
Authors: Maximilian K{\"o}per, Sabine Schulte im Walde
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: All

This paper presents a collection of 350,000 German lemmatised words, rated on four psycholinguistic affective attributes. All ratings were obtained via a supervised learning algorithm that can automatically calculate a numerical rating of a word. We applied this algorithm to abstractness, arousal, imageability and valence. Comparison with human ratings reveals high correlation across all rating types. The full resource is publically available at: http://www.ims.uni-stuttgart.de/data/affective{\_}norms/
Latin Vallex. A Treebank-based Semantic Valency Lexicon for Latin
Authors: Marco Passarotti, Berta Gonz{\'a}lez Saavedra, Christophe Onambele
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Despite a centuries-long tradition in lexicography, Latin lacks state-of-the-art computational lexical resources. This situation is strictly related to the still quite limited amount of linguistically annotated textual data for Latin, which can help the building of new lexical resources by supporting them with empirical evidence. However, projects for creating new language resources for Latin have been launched over the last decade to fill this gap. In this paper, we present Latin Vallex, a valency lexicon for Latin built in mutual connection with the semantic and pragmatic annotation of two Latin treebanks featuring texts of different eras. On the one hand, such a connection between the empirical evidence provided by the treebanks and the lexicon allows to enhance each frame entry in the lexicon with its frequency in real data. On the other hand, each valency-capable word in the treebanks is linked to a frame entry in the lexicon.
A Framework for Cross-lingual/Node-wise Alignment of Lexical-Semantic Resources
Authors: Yoshihiko Hayashi
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Relation

Given lexical-semantic resources in different languages, it is useful to establish cross-lingual correspondences, preferably with semantic relation labels, between the concept nodes in these resources. This paper presents a framework for enabling a cross-lingual/node-wise alignment of lexical-semantic resources, where cross-lingual correspondence candidates are first discovered and ranked, and then classified by a succeeding module. Indeed, we propose that a two-tier classifier configuration is feasible for the second module: the first classifier filters out possibly irrelevant correspondence candidates and the second classifier assigns a relatively fine-grained semantic relation label to each of the surviving candidates. The results of Japanese-to-English alignment experiments using EDR Electronic Dictionary and Princeton WordNet are described to exemplify the validity of the proposal.
Lexical Coverage Evaluation of Large-scale Multilingual Semantic Lexicons for Twelve Languages
Authors: Scott Piao, Paul Rayson, Dawn Archer, Francesca Bianchi, Carmen Dayrell, Mahmoud El-Haj, Ricardo-Mar{\'\i}a Jim{\'e}nez, Dawn Knight, Michal K{\v{r}}en, Laura L{\"o}fberg, Rao Muhammad Adeel Nawab, Jawad Shafi, Phoey Lee Teh, Olga Mudraya
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The last two decades have seen the development of various semantic lexical resources such as WordNet (Miller, 1995) and the USAS semantic lexicon (Rayson et al., 2004), which have played an important role in the areas of natural language processing and corpus-based studies. Recently, increasing efforts have been devoted to extending the semantic frameworks of existing lexical knowledge resources to cover more languages, such as EuroWordNet and Global WordNet. In this paper, we report on the construction of large-scale multilingual semantic lexicons for twelve languages, which employ the unified Lancaster semantic taxonomy and provide a multilingual lexical knowledge base for the automatic UCREL semantic annotation system (USAS). Our work contributes towards the goal of constructing larger-scale and higher-quality multilingual semantic lexical resources and developing corpus annotation tools based on them. Lexical coverage is an important factor concerning the quality of the lexicons and the performance of the corpus annotation tools, and in this experiment we focus on evaluating the lexical coverage achieved by the multilingual lexicons and semantic annotation tools based on them. Our evaluation shows that some semantic lexicons such as those for Finnish and Italian have achieved lexical coverage of over 90{\%} while others need further expansion.
Building Concept Graphs from Monolingual Dictionary Entries
Authors: G{\'a}bor Recski
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Semantic Textual Similarity

We present the dict{\_}to{\_}4lang tool for processing entries of three monolingual dictionaries of English and mapping definitions to concept graphs following the 4lang principles of semantic representation introduced by (Kornai, 2010). 4lang representations are domain- and language-independent, and make use of only a very limited set of primitives to encode the meaning of all utterances. Our pipeline relies on the Stanford Dependency Parser for syntactic analysis, the dep to 4lang module then builds directed graphs of concepts based on dependency relations between words in each definition. Several issues are handled by construction-specific rules that are applied to the output of dep{\_}to{\_}4lang. Manual evaluation suggests that ca. 75{\%} of graphs built from the Longman Dictionary are either entirely correct or contain only minor errors. dict{\_}to{\_}4lang is available under an MIT license as part of the 4lang library and has been used successfully in measuring Semantic Textual Similarity (Recski and {\'A}cs, 2015). An interactive demo of core 4lang functionalities is available at http://4lang.hlt.bme.hu.
Semantic Layer of the Valence Dictionary of Polish Walenty
Authors: El{\.z}bieta Hajnicz, Anna Andrzejczuk, Tomasz Bartosiak
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Attribute, Sentence

This article presents the semantic layer of Walentyâ€•a new valence dictionary of Polish predicates, with a number of novel features, as compared to other such dictionaries. The dictionary contains two layers, syntactic and semantic. The syntactic layer describes syntactic and morphosyntactic constraints predicates put on their dependants. In particular, it includes a comprehensive and powerful phraseological component. The semantic layer shows how predicates and their arguments are involved in a described situation in an utterance. These two layers are connected, representing how semantic arguments can be realised on the surface. Each syntactic schema and each semantic frame are illustrated by at least one exemplary sentence attested in linguistic reality. The semantic layer consists of semantic frames represented as lists of pairs and connected with PlWordNet lexical units. Semantic roles have a two-level representation (basic roles are provided with an attribute) enabling representation of arguments in a flexible way. Selectional preferences are based on PlWordNet structure as well.
Italian VerbNet: A Construction-based Approach to Italian Verb Classification
Authors: Lucia Busso, Aless Lenci, ro
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Classification, General Classification

This paper proposes a new method for Italian verb classification -and a preliminary example of resulting classes- inspired by Levin (1993) and VerbNet (Kipper-Schuler, 2005), yet partially independent from these resources; we achieved such a result by integrating Levin and VerbNet{'}s models of classification with other theoretic frameworks and resources. The classification is rooted in the constructionist framework (Goldberg, 1995; 2006) and is distribution-based. It is also semantically characterized by a link to FrameNet{'}ssemanticframesto represent the event expressed by a class. However, the new Italian classes maintain the hierarchic {``}tree{''} structure and monotonic nature of VerbNet{'}s classes, and, where possible, the original names (e.g.: Verbs of Killing, Verbs of Putting, etc.). We therefore propose here a taxonomy compatible with VerbNet but at the same time adapted to Italian syntax and semantics. It also addresses a number of problems intrinsic to the original classifications, such as the role of argument alternations, here regarded simply as epiphenomena, consistently with the constructionist approach.
A Large Rated Lexicon with French Medical Words
Authors: Natalia Grabar, Thierry Hamon
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Patients are often exposed to medical terms, such as anosognosia, myelodysplastic, or hepatojejunostomy, that can be semantically complex and hardly understandable by non-experts in medicine. Hence, it is important to assess which words are potentially non-understandable and require further explanations. The purpose of our work is to build specific lexicon in which the words are rated according to whether they are understandable or non-understandable. We propose to work with medical words in French such as provided by an international medical terminology. The terms are segmented in single words and then each word is manually processed by three annotators. The objective is to assign each word into one of the three categories: I can understand, I am not sure, I cannot understand. The annotators do not have medical training nor they present specific medical problems. They are supposed to represent an average patient. The inter-annotator agreement is then computed. The content of the categories is analyzed. Possible applications in which this lexicon can be helpful are proposed and discussed. The rated lexicon is freely available for the research purposes. It is accessible online at http://natalia.grabar.perso.sfr.fr/rated-lexicon.html
Best of Both Worlds: Making Word Sense Embeddings Interpretable
Authors: Alex Panchenko, er
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Word sense embeddings represent a word sense as a low-dimensional numeric vector. While this representation is potentially useful for NLP applications, its interpretability is inherently limited. We propose a simple technique that improves interpretability of sense vectors by mapping them to synsets of a lexical resource. Our experiments with AdaGram sense embeddings and BabelNet synsets show that it is possible to retrieve synsets that correspond to automatically learned sense vectors with Precision of 0.87, Recall of 0.42 and AUC of 0.78.
VerbLexPor: a lexical resource with semantic roles for Portuguese
Authors: Leonardo Zilio, Maria Jos{\'e} Bocorny Finatto, Aline Villavicencio
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Sentence

This paper presents a lexical resource developed for Portuguese. The resource contains sentences annotated with semantic roles. The sentences were extracted from two domains: Cardiology research papers and newspaper articles. Both corpora were analyzed with the PALAVRAS parser and subsequently processed with a subcategorization frames extractor, so that each sentence that contained at least one main verb was stored in a database together with its syntactic organization. The annotation was manually carried out by a linguist using an annotation interface. Both the annotated and non-annotated data were exported to an XML format, which is readily available for download. The reason behind exporting non-annotated data is that there is syntactic information collected from the parser annotation in the non-annotated data, and this could be useful for other researchers. The sentences from both corpora were annotated separately, so that it is possible to access sentences either from the Cardiology or from the newspaper corpus. The full resource presents more than seven thousand semantically annotated sentences, containing 192 different verbs and more than 15 thousand individual arguments and adjuncts.
A Multilingual Predicate Matrix
Authors: Maddalen Lopez de Lacalle, Egoitz Laparra, Itziar Aldabe, German Rigau
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents the Predicate Matrix 1.3, a lexical resource resulting from the integration of multiple sources of predicate information including FrameNet, VerbNet, PropBank and WordNet. This new version of the Predicate Matrix has been extended to cover nominal predicates by adding mappings to NomBank. Similarly, we have integrated resources in Spanish, Catalan and Basque. As a result, the Predicate Matrix 1.3 provides a multilingual lexicon to allow interoperable semantic analysis in multiple languages.
A Gold Standard for Scalar Adjectives
Authors: Bryan Wilkinson, Oates Tim
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present a gold standard for evaluating scale membership and the order of scalar adjectives. In addition to evaluating existing methods of ordering adjectives, this knowledge will aid in studying the organization of adjectives in the lexicon. This resource is the result of two elicitation tasks conducted with informants from Amazon Mechanical Turk. The first task is notable for gathering open-ended lexical data from informants. The data is analyzed using Cultural Consensus Theory, a framework from anthropology, to not only determine scale membership but also the level of consensus among the informants (Romney et al., 1986). The second task gathers a culturally salient ordering of the words determined to be members. We use this method to produce 12 scales of adjectives for use in evaluation.
VerbCROcean: A Repository of Fine-Grained Semantic Verb Relations for Croatian
Authors: Ivan Sekuli{\'c}, Jan {\v{S}}najder
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Relation

In this paper we describe VerbCROcean, a broad-coverage repository of fine-grained semantic relations between Croatian verbs. Adopting the methodology of Chklovski and Pantel (2004) used for acquiring the English VerbOcean, we first acquire semantically related verb pairs from a web corpus hrWaC by relying on distributional similarity of subject-verb-object paths in the dependency trees. We then classify the semantic relations between each pair of verbs as similarity, intensity, antonymy, or happens-before, using a number of manually-constructed lexico-syntatic patterns. We evaluate the quality of the resulting resource on a manually annotated sample of 1000 semantic verb relations. The evaluation revealed that the predictions are most accurate for the similarity relation, and least accurate for the intensity relation. We make available two variants of VerbCROcean: a coverage-oriented version, containing about 36k verb pairs at a precision of 41{\%}, and a precision-oriented version containing about 5k verb pairs, at a precision of 56{\%}.
Enriching a Portuguese WordNet using Synonyms from a Monolingual Dictionary
Authors: Alberto Sim{\~o}es, Xavier G{\'o}mez Guinovart, Jos{\'e} Jo{\~a}o Almeida
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this article we present an exploratory approach to enrich a WordNet-like lexical ontology with the synonyms present in a standard monolingual Portuguese dictionary. The dictionary was converted from PDF into XML and senses were automatically identified and annotated. This allowed us to extract them, independently of definitions, and to create sets of synonyms (synsets). These synsets were then aligned with WordNet synsets, both in the same language (Portuguese) and projecting the Portuguese terms into English, Spanish and Galician. This process allowed both the addition of new term variants to existing synsets, as to create new synsets for Portuguese.
Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The Serbian Movie Review Dataset
Authors: Vuk Batanovi{\'c}, Bo{\v{s}}ko Nikoli{\'c}, Milan Milosavljevi{\'c}
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: BIG-bench Machine Learning, Selection bias, Sentiment Analysis, Sentiment Classification

Collecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the whole population. Ignoring this bias leads to less robust machine learning classifiers and less reliable evaluation results. In this paper we present a dataset balancing algorithm that minimizes the sample selection bias by eliminating irrelevant systematic differences between the sentiment classes. We prove its superiority over the random sampling method and we use it to create the Serbian movie review dataset ― SerbMR ― the first balanced and topically uniform sentiment analysis dataset in Serbian. In addition, we propose an incremental way of finding the optimal combination of simple text processing options and machine learning features for sentiment classification. Several popular classifiers are used in conjunction with this evaluation approach in order to establish strong but reliable baselines for sentiment analysis in Serbian.
ANTUSD: A Large Chinese Sentiment Dictionary
Authors: Shih-Ming Wang, Lun-Wei Ku
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: General Classification

This paper introduces the augmented NTU sentiment dictionary, abbreviated as ANTUSD, which is constructed by collecting sentiment stats of words in several sentiment annotation work. A total of 26,021 words were collected in ANTUSD. For each word, the CopeOpi numerical sentiment score and the number of positive annotation, neutral annotation, negative annotation, non-opinionated annotation, and not-a-word annotation are provided. Words and their sentiment information in ANTUSD have been linked to the Chinese ontology E-HowNet to provide rich semantic information. We demonstrate the usage of ANTUSD in polarity classification of words, and the results show that a superior f-score 98.21 is achieved, which supports the usefulness of the ANTUSD. ANTUSD can be freely obtained through application from NLPSA lab, Academia Sinica: http://academiasinicanlplab.github.io/
Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation
Authors: Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), General Classification, Sentence, Sentiment Analysis, Sentiment Classification, Term Extraction

Due to the phenomenal growth of online product reviews, sentiment analysis (SA) has gained huge attention, for example, by online service providers. A number of benchmark datasets for a wide range of domains have been made available for sentiment analysis, especially in resource-rich languages. In this paper we assess the challenges of SA in Hindi by providing a benchmark setup, where we create an annotated dataset of high quality, build machine learning models for sentiment analysis in order to show the effective usage of the dataset, and finally make the resource available to the community for further advancement of research. The dataset comprises of Hindi product reviews crawled from various online sources. Each sentence of the review is annotated with aspect term and its associated sentiment. As classification algorithms we use Conditional Random Filed (CRF) and Support Vector Machine (SVM) for aspect term extraction and sentiment analysis, respectively. Evaluation results show the average F-measure of 41.07{\%} for aspect term extraction and accuracy of 54.05{\%} for sentiment classification.