Compositional Semantics using Feature-Based Models from WordNet

Gamallo, Pablo; Pereira-Fariña, Martín

doi:10.18653/v1/w17-1901

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…Different works have tried to introduce sense representations in the context of compositionality (Köper & im Walde, 2017;Kober, Weeds, Wilkie, Reffin, & Weir, 2017), with different degrees of success. The main idea is to select the intended sense of a word and only introduce that specific meaning into the composition, either through context-based sense induction (Thater, Fürstenau, & Pinkal, 2011), exemplar-based representation (Reddy, Klapaftis, McCarthy, & Manandhar, 2011), or with the help of external resources, such as WordNet (Gamallo & Pereira-Fariña, 2017). An example of the first type of approach can be found in Cheng and Kartsaklis (2015), where a recurrent neural network in which word embeddings were split into multiple sense vectors was proposed.…”

Section: Compositionalitymentioning

confidence: 99%

From Word To Sense Embeddings: A Survey on Vector Representations of Meaning

Camacho-Collados¹,

Pilehvar²

2018

jair

240

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Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.

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Section: Compositionalitymentioning

confidence: 99%

From Word To Sense Embeddings: A Survey on Vector Representations of Meaning

Camacho-Collados¹,

Pilehvar²

2018

jair

240

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“…In current work, we are defining richer semantic word models by combining WordNet features with semantic spaces based on distributional contexts (Gamallo and Pereira-Fariña, 2017). This hybrid method might also help overcome scarcity.…”

Section: Discussionmentioning

confidence: 99%

Sense Contextualization in a Dependency-Based Compositional Distributional Model

Gamallo¹

2017

Proceedings of the 2nd Workshop on Representation Learning for NLP

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Little attention has been paid to distributional compositional methods which employ syntactically structured vector models. As word vectors belonging to different syntactic categories have incompatible syntactic distributions, no trivial compositional operation can be applied to combine them into a new compositional vector. In this article, we generalize the method described by Erk and Padó (2009) by proposing a dependency-base framework that contextualize not only lemmas but also selectional preferences. The main contribution of the article is to expand their model to a fully compositional framework in which syntactic dependencies are put at the core of semantic composition. We claim that semantic composition is mainly driven by syntactic dependencies. Each syntactic dependency generates two new compositional vectors representing the contextualized sense of the two related lemmas. The sequential application of the compositional operations associated to the dependencies results in as many contextualized vectors as lemmas the composite expression contains. At the end of the semantic process, we do not obtain a single compositional vector representing the semantic denotation of the whole composite expression, but one contextualized vector for each lemma of the whole expression. Our method avoids the troublesome high-order tensor representations by defining lemmas and selectional restrictions as first-order tensors (i.e. standard vectors). A corpus-based experiment is performed to both evaluate the quality of the compositional vectors built with our strategy, and to compare them to other approaches on distributional compositional semantics. The experiments show that our dependency-based compositional method performs as (or even better than) the stateof-the-art.

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“…The resultant ontology of the proposed approach appeared to be reliable and was also verified by software engineers. Gamallo and Pereira-Farina [20] used WordNet knowledge structure for OL. Different WordNet relation types, such as synset and hypernyms, are exploited to learn the vocabulary of ontology.…”

Section: B Knowledge-based Ol Techniquesmentioning

confidence: 99%

Using Combined List Hierarchy and Headings of HTML Documents for Learning Domain-Specific Ontology

Raza¹,

Raza²,

Jabeen³

et al. 2020

IJACSA

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HTML pages contain unstructured and diverse information. However, these documents lack semantics and are not machine understandable. Semantic webs aim to add formal semantics to web data, whereas ontology provides formal semantics to a domain and is thus considered a foundation of semantic webs. Domain ontologies can be constructed manually, but this process is tedious and inefficient. Thus, this study presents an ontology learning (OL) model to create domain ontologies automatically from a set of HTML pages. The key insight of this research is that it combines the list structure and headings of HTML pages to recognize the ontology vocabulary. The approach also incorporates synonym relationships with ontology and allows the semantic interpretation of ontology concepts. We implement the proposed OL approach to build sports ontology from a collection of sports domain HTML documents. The new sports ontology is tested using FaCT++ reasoner; results show no inconsistency in the ontology. Furthermore, experts evaluate the successful mapping of HTML lists and headings to the ontology vocabulary. The proposed OL approach performs effectively and achieves 92.7% and 95.4% precision values for list and heading mapping, respectively.

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Compositional Semantics using Feature-Based Models from WordNet

Cited by 5 publications

References 31 publications

From Word To Sense Embeddings: A Survey on Vector Representations of Meaning

From Word To Sense Embeddings: A Survey on Vector Representations of Meaning

Sense Contextualization in a Dependency-Based Compositional Distributional Model

Using Combined List Hierarchy and Headings of HTML Documents for Learning Domain-Specific Ontology

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