| Challenge: | Existing word representations do not capture semantic similarity for bridging anaphora resolution. |
| Approach: | They propose to use word embeddings to capture semantic similarity by exploring syntactic structure of noun phrases. |
| Outcome: | The proposed model achieves 30% of accuracy for bridging anaphora resolution on ISNotes corpus. |
Similar Papers
A Deterministic Algorithm for Bridging Anaphora Resolution (D18-1)
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| Challenge: | Existing methods for bridging anaphora resolution only consider NPs’ head nouns and thus do not capture the semantics of NP. |
| Approach: | They propose a deterministic approach to bridging anaphora resolution which represents the semantics of an NP based on its head noun and modifications. |
| Outcome: | The proposed approach achieves competitive results compared to the best system in Hou et al. (2013) which explores Markov Logic Networks to model the problem. |
Bridging resolution: Task definition, corpus resources and rule-based experiments (C18-1)
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| Challenge: | Recent work on bridging resolution has been based on the corpus ISNotes, as this was the only corpus available with unrestricted bridling annotations. |
| Approach: | They propose a rule-based system to resolve bridging annotations in ISNotes corpus and apply it to new corpora. |
| Outcome: | The proposed system achieves state-of-the-art performance on ISNotes corpus, but low performance on in-domain corpora. |
Bridging Anaphora Resolution as Question Answering (2020.acl-main)
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| Challenge: | Existing studies on bridging anaphora resolution focus on question answering based on context . briding anaphorisms and their antecedents are linked via various lexico-semantic, frame or encyclopedic relations. |
| Approach: | They propose a question answering framework for bridging anaphora resolution . they propose briding anaphorisms and their antecedents are linked via various lexico-semantic, frame or encyclopedic relations. |
| Outcome: | The proposed method generates state-of-the-art results on two bridging corpora. |
Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources (2020.acl-srw)
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| Challenge: | In this paper, we present an effective method for semantic specialization of word vector representations. |
| Approach: | They propose a method for semantic specialization of word vector representations using BabelNet. |
| Outcome: | The proposed method improves on word similarity and dialog state tracking tasks. |
Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
Bridging Resolution: A Survey of the State of the Art (2020.coling-main)
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| Challenge: | bridging resolution is an anaphora resolution task that is less studied than entity coreference resolution. |
| Approach: | This paper presents a survey of the current state of research on bridging resolution . it identifies and resolves bridling/associative anaphors, which are anamorphic references to non-identical associated antecedents. |
| Outcome: | The proposed task is more difficult than entity coreference resolution because of the lack of annotated corpora and lack of standardized evaluation protocols. |
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
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| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
| Outcome: | The proposed architectures achieve comparable or better results compared to previous models without tying . the proposed architecture reduces parameters, enabling more compact models and faster learning. |
Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora (C18-1)
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| Challenge: | Recent studies on bilingual lexicon extraction from specialized comparable corpora show differences in performance . lack of large specialized corporan to build efficient representations can be partially explained . |
| Approach: | They propose to use character-based embedding models to combine different embeddable models . they emphasize how character-driven embeddance models outperform other models on quality . |
| Outcome: | The proposed model outperforms other models on quality of extracted bilingual lexicons . comparable corpora are an interesting and practical alternative to parallel corporation . |
From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources (C18-1)
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| Challenge: | Distributional word representations are omnipresent in modern NLP. |
| Approach: | They propose to combine lemmatization and part of speech (POS) typing to improve word embedding performance. |
| Outcome: | The proposed methods improve word embedding performance on verbs and verbs. |
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration (2021.emnlp-main)
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| Challenge: | Phrase representations derived from pretrained language models often lack lexical similarity to determine semantic relatedness. |
| Approach: | They propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings by fine- tuning a dataset of diverse phrasal paraphrases and a large-scale dataset of phrases in context. |
| Outcome: | The proposed model outperforms baseline models across phrase-level similarity tasks while also showing increased lexical diversity between nearest neighbors in the vector space. |