344 research outputs found
Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?
Despite the number of NLP studies dedicated to thematic fit estimation,
little attention has been paid to the related task of composing and updating
verb argument expectations. The few exceptions have mostly modeled this
phenomenon with structured distributional models, implicitly assuming a
similarly structured representation of events. Recent experimental evidence,
however, suggests that human processing system could also exploit an
unstructured "bag-of-arguments" type of event representation to predict
upcoming input. In this paper, we re-implement a traditional structured model
and adapt it to compare the different hypotheses concerning the degree of
structure in our event knowledge, evaluating their relative performance in the
task of the argument expectations update.Comment: conference paper, IWC
Measuring Thematic Fit with Distributional Feature Overlap
In this paper, we introduce a new distributional method for modeling
predicate-argument thematic fit judgments. We use a syntax-based DSM to build a
prototypical representation of verb-specific roles: for every verb, we extract
the most salient second order contexts for each of its roles (i.e. the most
salient dimensions of typical role fillers), and then we compute thematic fit
as a weighted overlap between the top features of candidate fillers and role
prototypes. Our experiments show that our method consistently outperforms a
baseline re-implementing a state-of-the-art system, and achieves better or
comparable results to those reported in the literature for the other
unsupervised systems. Moreover, it provides an explicit representation of the
features characterizing verb-specific semantic roles.Comment: 9 pages, 2 figures, 5 tables, EMNLP, 2017, thematic fit, selectional
preference, semantic role, DSMs, Distributional Semantic Models, Vector Space
Models, VSMs, cosine, APSyn, similarity, prototyp
Distributional Semantics Today Introduction to the special issue
International audienceThis introduction to the special issue of the TAL journal on distributional semantics provides an overview of the current topics of this field and gives a brief summary of the contributions. RÉSUMÉ. Cette introduction au numéro spécial de la revue TAL consacré à la sémantique dis-tributionnelle propose un panorama des thèmes de recherche actuels dans ce champ et fournit un résumé succinct des contributions acceptées
Representing Verbs with Visual Argument Vectors
Is it possible to use images to model verb semantic similarities? Starting from this core question, we developed two textual distributionalsemantic models and a visual one. We found it particularly interesting and challenging to investigate this Part of Speech since verbsare not often analysed in researches focused on multimodal distributional semantics. After the creation of the visual and textualdistributional space, the three models were evaluated in relation to SimLex-999, a gold standard resource. Through this evaluation,we demonstrate that, using visual distributional models, it is possible to extract meaningful information and to effectively capture thesemantic similarity between verbs
Distributional Semantics Today
This introduction to the special issue of the TAL journal on distributional semantics provides an overview of the current topics of this field and gives a brief summary of the contribution
The Effects of Data Size and Frequency Range on Distributional Semantic Models
This paper investigates the effects of data size
and frequency range on distributional seman-
tic models. We compare the performance of
a number of representative models for several
test settings over data of varying sizes, and
over test items of various frequency. Our re-
sults show that neural network-based models
underperform when the data is small, and that
the most reliable model over data of varying
sizes and frequency ranges is the inverted fac-
torized mode
Italian VerbNet: A Construction based Approach to Italian Verb 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
PISA: A measure of Preference In Selection of Arguments to model verb argument recoverability
Our paper offers a computational model ofthe semantic recoverability of verb arguments,tested in particular on direct objects and In-struments. Our fully distributional model isintended to improve on older taxonomy-basedmodels, which require a lexicon in addition tothe training corpus. We computed the selec-tional preferences of 99 transitive verbs and173 Instrument verbs as the mean value of thepairwise cosine similarity between their argu-ments (a weighted mean between all the argu-ments, or an unweighted mean with the top-mostkarguments).Results show that ourmodel can predict the recoverability of objectsand Instruments, providing a similar result tothat of taxonomy-based models but at a muchcheaper computational cost
Less is MORE: a MultimOdal system for tag REfinement
With the proliferation of image-based social media, an ex-tremely large amount of multimodal data is being produced. Very oftenimage contents are published together with a set of user defined meta-data such as tags and textual descriptions. Despite being very useful toenhance traditional image retrieval, user defined tags on social mediahave been proven to be noneffective to index images because they areinfluenced by personal experiences of the owners as well as their will ofpromoting the published contents. To be analyzed and indexed, multi-modal data require algorithms able to jointly deal with textual and visualdata. This research presents a multimodal approach to the problem of tagrefinement, which consists in separating the relevant descriptors (tags)of images from noisy ones. The proposed method exploits both Natu-ral Language Processing (NLP) and Computer Vision (CV) techniquesbased on deep learning to find a match between the textual informationand visual content of social media posts. Textual semantic features arerepresented with (multilingual) word embeddings, while visual ones areobtained with image classification. The proposed system is evaluated ona manually annotated Italian dataset extracted from Instagram achieving68% of weighted F1-scor
Don’t Invite BERT to Drink a Bottle: Modeling the Interpretation of Metonymies Using BERT and Distributional Representations
In this work, we carry out two experiments in order to assess the ability of BERT to capture themeaning shift associated with metonymic expressions. We test the model on a new dataset that isrepresentative of the most common types of metonymy. We compare BERT with the StructuredDistributional Model (SDM), a model for the representation of words in context which is basedon the notion of Generalized Event Knowledge. The results reveal that, while BERT abilityto deal with metonymy is quite limited, SDM is good at predicting the meaning of metonymicexpressions, providing support for an account of metonymy based on event knowledge
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