pwc-archive/papers-with-abstracts huggingface dataset:
Towards a lexicon of event-selecting predicates for a French FactBank
Authors: Ingrid Falk, Fabienne Martin
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

This paper presents ongoing work for the construction of a French FactBank and a lexicon of French event-selecting predicates (ESPs), by applying the factuality detection algorithm introduced in (Saur{\'\i} and Pustejovsky, 2012). This algorithm relies on a lexicon of ESPs, specifying how these predicates influence the polarity of their embedded events. For this pilot study, we focused on French factive and implicative verbs, and capitalised on a lexical resource for the English counterparts of these verbs provided by the CLSI Group (Nairn et al., 2006; Karttunen, 2012).
Universal Dependencies to Logical Form with Negation Scope
Authors: Federico Fancellu, Siva Reddy, Adam Lopez, Bonnie Webber
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Form, Negation

Many language technology applications would benefit from the ability to represent negation and its scope on top of widely-used linguistic resources. In this paper, we investigate the possibility of obtaining a first-order logic representation with negation scope marked using \textit{Universal Dependencies}. To do so, we enhance \textit{UDepLambda}, a framework that converts dependency graphs to logical forms. The resulting \textit{UDepLambda$\lnot$ }is able to handle phenomena related to scope by means of an higher-order type theory, relevant not only to negation but also to universal quantification and other complex semantic phenomena. The initial conversion we did for English is promising, in that one can represent the scope of negation also in the presence of more complex phenomena such as universal quantifiers.
Meaning Banking beyond Events and Roles
Authors: Johan Bos
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

In this talk I will discuss the analysis of several semantic phenomena that need meaning representations that can describe attributes of propositional contexts. I will do this in a version of Discourse Representation Theory, using a universal semantic tagset developed as part of a project that aims to produce a large meaning bank (a semantically-annotated corpus) for four languages (English, Dutch, German and Italian).
The Scope and Focus of Negation: A Complete Annotation Framework for Italian
Authors: Bego{\~n}a Altuna, Anne-Lyse Minard, Manuela Speranza
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Articles, Decision Making, Negation, Sentiment Analysis

In this paper we present a complete framework for the annotation of negation in Italian, which accounts for both negation scope and negation focus, and also for language-specific phenomena such as negative concord. In our view, the annotation of negation complements more comprehensive Natural Language Processing tasks, such as temporal information processing and sentiment analysis. We applied the proposed framework and the guidelines built on top of it to the annotation of written texts, namely news articles and tweets, thus producing annotated data for a total of over 36,000 tokens.
Annotation of negation in the IULA Spanish Clinical Record Corpus
Authors: Montserrat Marimon, Jorge Vivaldi, N{\'u}ria Bel
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Medical Diagnosis, Negation, Negation Detection, Term Extraction

This paper presents the IULA Spanish Clinical Record Corpus, a corpus of 3,194 sentences extracted from anonymized clinical records and manually annotated with negation markers and their scope. The corpus was conceived as a resource to support clinical text-mining systems, but it is also a useful resource for other Natural Language Processing systems handling clinical texts: automatic encoding of clinical records, diagnosis support, term extraction, among others, as well as for the study of clinical texts. The corpus is publicly available with a CC-BY-SA 3.0 license.
Annotating Negation in Spanish Clinical Texts
Authors: Noa Cruz, Roser Morante, Manuel J. Ma{\~n}a L{\'o}pez, Jacinto Mata V{\'a}zquez, Carlos L. Parra Calder{\'o}n
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Negation

In this paper we present on-going work on annotating negation in Spanish clinical documents. A corpus of anamnesis and radiology reports has been annotated by two domain expert annotators with negation markers and negated events. The Dice coefficient for inter-annotator agreement is higher than 0.94 for negation markers and higher than 0.72 for negated events. The corpus will be publicly released when the annotation process is finished, constituting the first corpus annotated with negation for Spanish clinical reports available for the NLP community.
Neural Networks for Negation Cue Detection in Chinese
Authors: Hangfeng He, Federico Fancellu, Bonnie Webber
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Feature Engineering, Negation, Sentence, Word Alignment

Negation cue detection involves identifying the span inherently expressing negation in a negative sentence. In Chinese, negative cue detection is complicated by morphological proprieties of the language. Previous work has shown that negative cue detection in Chinese can benefit from specific lexical and morphemic features, as well as cross-lingual information. We show here that they are not necessary: A bi-directional LSTM can perform equally well, with minimal feature engineering. In particular, the use of a character-based model allows us to capture characteristics of negation cues in Chinese using word-embedding information only. Not only does our model performs on par with previous work, further error analysis clarifies what problems remain to be addressed.
An open-source tool for negation detection: a maximum-margin approach
Authors: Martine Enger, Erik Velldal, Lilja {\O}vrelid
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: General Classification, Negation, Negation Detection, Structured Prediction

This paper presents an open-source toolkit for negation detection. It identifies negation cues and their corresponding scope in either raw or parsed text using maximum-margin classification. The system design draws on best practice from the existing literature on negation detection, aiming for a simple and portable system that still achieves competitive performance. Pre-trained models and experimental results are provided for English.
Compositional Semantics using Feature-Based Models from WordNet
Authors: Pablo Gamallo, Mart{\'\i}n Pereira-Fari{\~n}a
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

This article describes a method to build semantic representations of composite expressions in a compositional way by using WordNet relations to represent the meaning of words. The meaning of a target word is modelled as a vector in which its semantically related words are assigned weights according to both the type of the relationship and the distance to the target word. Word vectors are compositionally combined by syntactic dependencies. Each syntactic dependency triggers two complementary compositional functions: the named head function and dependent function. The experiments show that the proposed compositional method outperforms the state-of-the-art for both intransitive subject-verb and transitive subject-verb-object constructions.
Automated WordNet Construction Using Word Embeddings
Authors: Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Information Retrieval, Machine Translation, Translation, Word Embeddings, Word Sense Induction

We present a fully unsupervised method for automated construction of WordNets based upon recent advances in distributional representations of sentences and word-senses combined with readily available machine translation tools. The approach requires very few linguistic resources and is thus extensible to multiple target languages. To evaluate our method we construct two 600-word testsets for word-to-synset matching in French and Russian using native speakers and evaluate the performance of our method along with several other recent approaches. Our method exceeds the best language-specific and multi-lingual automated WordNets in F-score for both languages. The databases we construct for French and Russian, both languages without large publicly available manually constructed WordNets, will be publicly released along with the testsets.
Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses
Authors: Maximilian K{\"o}per, Sabine Schulte im Walde
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Semantic Textual Similarity, Topic Models, Word Embeddings

Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words. Among other applications, the degree of abstractness has been shown to be a useful information for metaphor detection. Our contribution to this topic are as follows: i) we compare supervised techniques to learn and extend abstractness ratings for huge vocabularies ii) we learn and investigate norms for larger units by propagating abstractness to verb-noun pairs which lead to better metaphor detection iii) we overcome the limitation of learning a single rating per word and show that multi-sense abstractness ratings are potentially useful for metaphor detection. Finally, with this paper we publish automatically created abstractness norms for 3million English words and multi-words as well as automatically created sense specific abstractness ratings
Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge
Authors: Yuan Ling, Yuan An, Sadid Hasan
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Information Retrieval, Question Answering

This paper presents a novel approach to the task of automatically inferring the most probable diagnosis from a given clinical narrative. Structured Knowledge Bases (KBs) can be useful for such complex tasks but not sufficient. Hence, we leverage a vast amount of unstructured free text to integrate with structured KBs. The key innovative ideas include building a concept graph from both structured and unstructured knowledge sources and ranking the diagnosis concepts using the enhanced word embedding vectors learned from integrated sources. Experiments on the TREC CDS and HumanDx datasets showed that our methods improved the results of clinical diagnosis inference.
Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations
Authors: Kentaro Kanada, Tetsunori Kobayashi, Yoshihiko Hayashi
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: General Classification, Multi-class Classification, Relation, Relation Classification

This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming: (1) a specific type of lexical-semantic relation between a pair of words would be indicated by a carefully designed set of relation-specific similarities associated with the words; and (2) the similarities could be effectively computed by {``}sense representations{''} (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations.
Supervised and unsupervised approaches to measuring usage similarity
Authors: Milton King, Paul Cook
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: LEMMA, Word Sense Disambiguation, Word Sense Induction

Usage similarity (USim) is an approach to determining word meaning in context that does not rely on a sense inventory. Instead, pairs of usages of a target lemma are rated on a scale. In this paper we propose unsupervised approaches to USim based on embeddings for words, contexts, and sentences, and achieve state-of-the-art results over two USim datasets. We further consider supervised approaches to USim, and find that although they outperform unsupervised approaches, they are unable to generalize to lemmas that are unseen in the training data.
Lexical Disambiguation of Igbo using Diacritic Restoration
Authors: Ignatius Ezeani, Mark Hepple, Ikechukwu Onyenwe
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: BIG-bench Machine Learning, General Classification

Properly written texts in Igbo, a low-resource African language, are rich in both orthographic and tonal diacritics. Diacritics are essential in capturing the distinctions in pronunciation and meaning of words, as well as in lexical disambiguation. Unfortunately, most electronic texts in diacritic languages are written without diacritics. This makes diacritic restoration a necessary step in corpus building and language processing tasks for languages with diacritics. In our previous work, we built some n-gram models with simple smoothing techniques based on a closed-world assumption. However, as a classification task, diacritic restoration is well suited for and will be more generalisable with machine learning. This paper, therefore, presents a more standard approach to dealing with the task which involves the application of machine learning algorithms.
Creating and Validating Multilingual Semantic Representations for Six Languages: Expert versus Non-Expert Crowds
Authors: Mahmoud El-Haj, Paul Rayson, Scott Piao, Stephen Wattam
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Named Entity Recognition (NER), Sentiment Analysis, Word Sense Disambiguation

Creating high-quality wide-coverage multilingual semantic lexicons to support knowledge-based approaches is a challenging time-consuming manual task. This has traditionally been performed by linguistic experts: a slow and expensive process. We present an experiment in which we adapt and evaluate crowdsourcing methods employing native speakers to generate a list of coarse-grained senses under a common multilingual semantic taxonomy for sets of words in six languages. 451 non-experts (including 427 Mechanical Turk workers) and 15 expert participants semantically annotated 250 words manually for Arabic, Chinese, English, Italian, Portuguese and Urdu lexicons. In order to avoid erroneous (spam) crowdsourced results, we used a novel task-specific two-phase filtering process where users were asked to identify synonyms in the target language, and remove erroneous senses.
Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation
Authors: Alex Panchenko, er, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Machine Translation, Translation, Word Embeddings, Word Sense Disambiguation

We introduce a new method for unsupervised knowledge-based word sense disambiguation (WSD) based on a resource that links two types of sense-aware lexical networks: one is induced from a corpus using distributional semantics, the other is manually constructed. The combination of two networks reduces the sparsity of sense representations used for WSD. We evaluate these enriched representations within two lexical sample sense disambiguation benchmarks. Our results indicate that (1) features extracted from the corpus-based resource help to significantly outperform a model based solely on the lexical resource; (2) our method achieves results comparable or better to four state-of-the-art unsupervised knowledge-based WSD systems including three hybrid systems that also rely on text corpora. In contrast to these hybrid methods, our approach does not require access to web search engines, texts mapped to a sense inventory, or machine translation systems.
Elucidating Conceptual Properties from Word Embeddings
Authors: Kyoung-Rok Jang, Sung-Hyon Myaeng
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Decision Making, Named Entity Recognition (NER), Sentiment Analysis, Word Embeddings

In this paper, we introduce a method of identifying the components (i.e. dimensions) of word embeddings that strongly signifies properties of a word. By elucidating such properties hidden in word embeddings, we could make word embeddings more interpretable, and also could perform property-based meaning comparison. With the capability, we can answer questions like {``}To what degree a given word has the property cuteness?{''} or {``}In what perspective two words are similar?{''}. We verify our method by examining how the strength of property-signifying components correlates with the degree of prototypicality of a target word.
TTCS$^\mathcalE$: a Vectorial Resource for Computing Conceptual Similarity
Authors: Enrico Mensa, Daniele P. Radicioni, Antonio Lieto
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Common Sense Reasoning

In this paper we introduce the TTCS$^{\mathcal{E}}$, a linguistic resource that relies on BabelNet, NASARI and ConceptNet, that has now been used to compute the conceptual similarity between concept pairs. The conceptual representation herein provides uniform access to concepts based on BabelNet synset IDs, and consists of a vector-based semantic representation which is compliant with the Conceptual Spaces, a geometric framework for common-sense knowledge representation and reasoning. The TTCS$^{\mathcal{E}}$ has been evaluated in a preliminary experimentation on a conceptual similarity task.
Measuring the Italian-English lexical gap for action verbs and its impact on translation
Authors: Lorenzo Gregori, Aless Panunzi, ro
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Translation

This paper describes a method to measure the lexical gap of action verbs in Italian and English by using the IMAGACT ontology of action. The fine-grained categorization of action concepts of the data source allowed to have wide overview of the relation between concepts in the two languages. The calculated lexical gap for both English and Italian is about 30{\%} of the action concepts, much higher than previous results. Beyond this general numbers a deeper analysis has been performed in order to evaluate the impact that lexical gaps can have on translation. In particular a distinction has been made between the cases in which the presence of a lexical gap affects translation correctness and completeness at a semantic level. The results highlight a high percentage of concepts that can be considered hard to translate (about 18{\%} from English to Italian and 20{\%} from Italian to English) and confirms that action verbs are a critical lexical class for translation tasks.
Word Sense Filtering Improves Embedding-Based Lexical Substitution
Authors: Anne Cocos, Marianna Apidianaki, Chris Callison-Burch
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Clustering, Entity Extraction using GAN, Part-Of-Speech Tagging, Semantic Textual Similarity, Sentiment Analysis, Word Embeddings, Word Sense Disambiguation

The role of word sense disambiguation in lexical substitution has been questioned due to the high performance of vector space models which propose good substitutes without explicitly accounting for sense. We show that a filtering mechanism based on a sense inventory optimized for substitutability can improve the results of these models. Our sense inventory is constructed using a clustering method which generates paraphrase clusters that are congruent with lexical substitution annotations in a development set. The results show that lexical substitution can still benefit from senses which can improve the output of vector space paraphrase ranking models.
Supervised and Unsupervised Word Sense Disambiguation on Word Embedding Vectors of Unambigous Synonyms
Authors: Aleks Wawer, er, Agnieszka Mykowiecka
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Word Embeddings, Word Sense Disambiguation

This paper compares two approaches to word sense disambiguation using word embeddings trained on unambiguous synonyms. The first is unsupervised method based on computing log probability from sequences of word embedding vectors, taking into account ambiguous word senses and guessing correct sense from context. The second method is supervised. We use a multilayer neural network model to learn a context-sensitive transformation that maps an input vector of ambiguous word into an output vector representing its sense. We evaluate both methods on corpora with manual annotations of word senses from the Polish wordnet (plWordnet).
The Use of Object Labels and Spatial Prepositions as Keywords in a Web-Retrieval-Based Image Caption Generation System
Authors: Br Birmingham, on, Adrian Muscat
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Caption Generation, Image Retrieval, Object, Retrieval, Sentence, Text Generation

In this paper, a retrieval-based caption generation system that searches the web for suitable image descriptions is studied. Google{'}s reverse image search is used to find potentially relevant web multimedia content for query images. Sentences are extracted from web pages and the likelihood of the descriptions is computed to select one sentence from the retrieved text documents. The search mechanism is modified to replace the caption generated by Google with a caption composed of labels and spatial prepositions as part of the query{'}s text alongside the image. The object labels are obtained using an off-the-shelf R-CNN and a machine learning model is developed to predict the prepositions. The effect on the caption generation system performance when using the generated text is investigated. Both human evaluations and automatic metrics are used to evaluate the retrieved descriptions. Results show that the web-retrieval-based approach performed better when describing single-object images with sentences extracted from stock photography websites. On the other hand, images with two image objects were better described with template-generated sentences composed of object labels and prepositions.
Learning to Recognize Animals by Watching Documentaries: Using Subtitles as Weak Supervision
Authors: Aparna Nurani Venkitasubramanian, Tinne Tuytelaars, Marie-Francine Moens
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Image Segmentation, object-detection, Object Detection, Semantic Segmentation

We investigate animal recognition models learned from wildlife video documentaries by using the weak supervision of the textual subtitles. This is a particularly challenging setting, since i) the animals occur in their natural habitat and are often largely occluded and ii) subtitles are to a large degree complementary to the visual content, providing a very weak supervisory signal. This is in contrast to most work on integrated vision and language in the literature, where textual descriptions are tightly linked to the image content, and often generated in a curated fashion for the task at hand. In particular, we investigate different image representations and models, including a support vector machine on top of activations of a pretrained convolutional neural network, as well as a Naive Bayes framework on a {`}bag-of-activations{'} image representation, where each element of the bag is considered separately. This representation allows key components in the image to be isolated, in spite of largely varying backgrounds and image clutter, without an object detection or image segmentation step. The methods are evaluated based on how well they transfer to unseen camera-trap images captured across diverse topographical regions under different environmental conditions and illumination settings, involving a large domain shift.