A Crowdsourced Frame Disambiguation Corpus with Ambiguity

We present a resource for the task of FrameNet semantic frame disambiguation of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations were collected using a novel crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. In contrast to the typical approach of attributing the best single frame to each word, we provide a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. This is based on the idea that inter-annotator disagreement is at least partly caused by ambiguity that is inherent to the text and frames. We have found many examples where the semantics of individual frames overlap sufficiently to make them acceptable alternatives for interpreting a sentence. We have argued that ignoring this ambiguity creates an overly arbitrary target for training and evaluating natural language processing systems - if humans cannot agree, why would we expect the correct answer from a machine to be any different? To process this data we also utilized an expanded lemma-set provided by the Framester system, which merges FN with WordNet to enhance coverage. Our dataset includes annotations of 1,000 sentence-word pairs whose lemmas are not part of FN. Finally we present metrics for evaluating frame disambiguation systems that account for ambiguity.Comment: Accepted to NAACL-HLT201

Capturing Ambiguity in Crowdsourcing Frame Disambiguation

FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words. In this paper, we present an approach to gather frame disambiguation annotations in sentences using a crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. We perform an experiment over a set of 433 sentences annotated with frames from the FrameNet corpus, and show that the aggregated crowd annotations achieve an F1 score greater than 0.67 as compared to expert linguists. We highlight cases where the crowd annotation was correct even though the expert is in disagreement, arguing for the need to have multiple annotators per sentence. Most importantly, we examine cases in which crowd workers could not agree, and demonstrate that these cases exhibit ambiguity, either in the sentence, frame, or the task itself, and argue that collapsing such cases to a single, discrete truth value (i.e. correct or incorrect) is inappropriate, creating arbitrary targets for machine learning.Comment: in publication at the sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 201

A Knowledge Engineering Primer

The aim of this primer is to introduce the subject of knowledge engineering in a concise but synthetic way to develop the reader's intuition about the area

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation

Strategies to Address Data Sparseness in Implicit Semantic Role Labeling

Natural language texts frequently contain predicates whose complete understanding re- quires access to other parts of the discourse. Human readers can retrieve such infor- mation across sentence boundaries and infer the implicit piece of information. This capability enables us to understand complicated texts without needing to repeat the same information in every single sentence. However, for computational systems, resolv- ing such information is problematic because computational approaches traditionally rely on sentence-level processing and rarely take into account the extra-sentential context. In this dissertation, we investigate this omission phenomena, called implicit semantic role labeling. Implicit semantic role labeling involves identification of predicate argu- ments that are not locally realized but are resolvable from the context. For example, in ”What’s the matter, Walters? asked Baynes sharply.”, the ADDRESSEE of the predicate ask, Walters, is not mentioned as one of its syntactic arguments, but can be recoverable from the previous sentence. In this thesis, we try to improve methods for the automatic processing of such predicate instances to improve natural language pro- cessing applications. Our main contribution is introducing approaches to solve the data sparseness problem of the task. We improve automatic identification of implicit roles by increasing the amount of training set without needing to annotate new instances. For this purpose, we propose two approaches. As the first one, we use crowdsourcing to annotate instances of implicit semantic roles and show that with an appropriate task de- sign, reliable annotation of implicit semantic roles can be obtained from the non-experts without the need to present precise and linguistic definition of the roles to them. As the second approach, we combine seemingly incompatible corpora to solve the problem of data sparseness of ISRL by applying a domain adaptation technique. We show that out of domain data from a different genre can be successfully used to improve a baseline implicit semantic role labeling model, when used with an appropriate domain adapta- tion technique. The results also show that the improvement occurs regardless of the predicate part of speech, that is, identification of implicit roles relies more on semantic features than syntactic ones. Therefore, annotating instances of nominal predicates, for instance, can help to improve identification of verbal predicates’ implicit roles, we well. Our findings also show that the variety of the additional data is more important than its size. That is, increasing a large amount of data does not necessarily lead to a better model

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation

Annotation en rôles sémantiques du français en domaine spécifique

In this Natural Language Processing Ph. D. Thesis, we aim to perform semantic role labeling on French domain-specific texts. This task first disambiguates the sense of predicates in a given text and annotates its child chunks with semantic roles such as Agent, Patient or Destination. The task helps many applications in domains where annotated corpora exist, but is difficult to use otherwise. We first evaluate on the FrameNet corpus an existing method based on VerbNet, which explains why the method is domain-independant. We show that substantial improvements can be obtained. We first use syntactic information by handling the passive voice. Next, we use semantic informations by taking advantage of the selectional restrictions present in VerbNet. To apply this method to French, we first translate lexical resources. We first translate the WordNet lexical database. Next, we translate the VerbNet lexicon which is organized semantically using syntactic information. We obtain its translation, VerbeNet, by reusing two French verb lexicons (the Lexique-Grammaire and Les Verbes Français) and by manually modifying and reorganizing the resulting lexicon. Finally, once those building blocks are in place, we evaluate the feasibility of semantic role labeling of French and English in three specific domains. We study the pros and cons of using VerbNet and VerbeNet to annotate those domains before explaining our future work.Cette thèse de Traitement Automatique des Langues a pour objectif l'annotation automatique en rôles sémantiques du français en domaine spécifique. Cette tâche désambiguïse le sens des prédicats d'un texte et annote les syntagmes liés avec des rôles sémantiques tels qu'Agent, Patient ou Destination. Elle aide de nombreuses applications dans les domaines où des corpus annotés existent, mais est difficile à utiliser quand ce n'est pas le cas. Nous avons d'abord évalué sur le corpus FrameNet une méthode existante d'annotation basée uniquement sur VerbNet et donc indépendante du domaine considéré. Nous montrons que des améliorations conséquentes peuvent être obtenues à la fois d'un point de vue syntaxique avec la prise en compte de la voix passive et d'un point de vue sémantique en utilisant les restrictions de sélection indiquées dans VerbNet. Pour utiliser cette méthode en français, nous traduisons deux ressources lexicales anglaises. Nous commençons par la base de données lexicales WordNet. Nous traduisons ensuite le lexique VerbNet dans lequel les verbes sont regroupés sémantiquement grâce à leurs traits syntaxiques. La traduction, VerbeNet, a été obtenue en réutilisant deux lexiques verbaux du français (le Lexique-Grammaire et Les Verbes Français) puis en modifiant manuellement l'ensemble des informations obtenues. Enfin, une fois ces briques en place, nous évaluons la faisabilité de l'annotation en rôles sémantiques en anglais et en français dans trois domaines spécifiques. Nous évaluons quels sont les avantages et inconvénients de se baser sur VerbNet et VerbeNet pour annoter ces domaines, avant d'indiquer nos perspectives pour poursuivre ces travaux

Knowledge Patterns for the Web: extraction, tranformation and reuse

This thesis aims at investigating methods and software architectures for discovering what are the typical and frequently occurring structures used for organizing knowledge in the Web. We identify these structures as Knowledge Patterns (KPs). KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Then we present K~ore, a software architecture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization

Orchestrating Game Generation

The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an audience of experts from within the same domain. What happens, however, when the creative process involves more than one creative domain such as in a digital game? How should the different domains influence each other so that the final outcome achieves a harmonized and fruitful communication across domains? How can a computational process orchestrate the various computational creators of the corresponding domains so that the final game has the desired functional and aesthetic characteristics? To address these questions, this article identifies game facet orchestration as the central challenge for AI-based game generation, discusses its dimensions and reviews research in automated game generation that has aimed to tackle it. In particular, we identify the different creative facets of games, we propose how orchestration can be facilitated in a top-down or bottom-up fashion, we review indicative preliminary examples of orchestration, and we conclude by discussing the open questions and challenges ahead

Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources

Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations