| Challenge: | Iterative term set expansion methods for distributional semantic models are used to label terms belonging to a sought after term set. |
| Approach: | They compare iterative term set expansion methods for distributional semantic models to the Simple Margin method, an active learning approach to classification using Support Vector Machines. |
| Outcome: | The proposed methods outperform centrality and classification based methods for distributional semantic models over five different term sets. |
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SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings (C18-2)
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Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan, Yoav Goldberg, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
| Challenge: | SetExpander is a corpus-based system for expanding a seed set of terms into a more complete set of words belonging to the same semantic class. |
| Approach: | They propose to use a corpus-based system for expanding a seed set of terms into a more complete set of words that belong to the same semantic class. |
| Outcome: | The proposed system can expand a seed set of terms, validate it, re-expand the expanded set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. |
Term Set Expansion based NLP Architect by Intel AI Lab (D18-2)
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Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
| Challenge: | SetExpander is a corpus-based system for expanding a seed set of terms into a more complete set of words belonging to the same semantic class. |
| Approach: | They propose a corpus-based system for expanding a seed set of terms into a more complete set of words that belong to the same semantic class. |
| Outcome: | The proposed system can expand a seed set of terms into a more complete set of words belonging to the same semantic class. |
A Two-Stage Masked LM Method for Term Set Expansion (2020.acl-main)
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| Challenge: | Existing methods for Term Set Expansion are either distributional or pattern-based . Term set expansion is a task of expanding a small seed set of example terms into a larger set of terms that belong to the same semantic category. |
| Approach: | They propose a method which uses neural masked language models to expand a small seed set of terms into a larger set of semantic terms. |
| Outcome: | The proposed method outperforms state-of-the-art methods due to the small seed set size . it uses neural masked language models to query large, pre-trained mlms . |
Empower Entity Set Expansion via Language Model Probing (2020.acl-main)
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| Challenge: | Existing methods for expanding seed entities with new entities belong to the same semantic class are difficult to implement and can lead to accumulative errors. |
| Approach: | They propose an iterative set expansion framework that leverages automatically generated class names to address the semantic drift issue. |
| Outcome: | The proposed framework generates high-quality class names and outperforms state-of-the-art methods significantly. |
Low-resource Entity Set Expansion: A Comprehensive Study on User-generated Text (2022.findings-naacl)
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| Challenge: | Existing benchmarks for entity set expansion (ESE) are limited to well-formed text and well-defined concepts. |
| Approach: | They propose to use user-generated text to assess the generalizability of ESE methods by identifying phenomena such as non-named entities, multifaceted entities and vague concepts. |
| Outcome: | The proposed methods are based on user-generated text to assess their generalizability and performance. |
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models (D19-61)
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| Challenge: | Word embeddings are an essential component of many natural language processing applications. |
| Approach: | They propose 3 new tasks to obtain higher-quality vectors for word embeddings . they use word forms in training data that are related to word forms themselves . |
| Outcome: | The proposed methods improve the performance of both baseline and advanced models on 4 out of 6 tasks. |
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery (2020.emnlp-main)
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| Challenge: | Entity set expansion and synonym discovery are two critical NLP tasks that are often performed separately, without exploring their interdependencies. |
| Approach: | They propose a framework that enables two tasks to mutually enhance each other by including popular entities’ infrequent synonyms into the set, which boosts set expansion recall. |
| Outcome: | The proposed framework can be used to enhance two NLP tasks by including popular entities’ infrequent synonyms into the set, which boosts set expansion recall. |
Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction (2021.emnlp-main)
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| Challenge: | Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. |
| Approach: | They perform an exhaustive and large-scale empirical comparison of term weighting methods in the context of keyword extraction using tf-idf. |
| Outcome: | The proposed methods have advantages over tf-idf, and qualitative differences between them. |
Short-Term Meaning Shift: A Distributional Exploration (N19-1)
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| Challenge: | a new study examines the phenomenon of short-term meaning shift in online communities . the authors use distributional representations to explore the phenomenon . |
| Approach: | They propose to use distributional representations to explore short-term meaning shift in online communities. |
| Outcome: | The proposed model has problems distinguishing meaning shift from referential phenomena, and measures contextual variability to remedy this. |
What are the Goals of Distributional Semantics? (2020.acl-main)
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| Challenge: | a new paper examines distributional semantic models' ability to deal with semantic challenges . authors argue that assessing progress in any field requires explicit long-term goals . |
| Approach: | They propose a broad linguistic perspective to assess distributional semantic models' ability to deal with various semantic challenges. |
| Outcome: | The proposed models can handle various semantic challenges, but they need to be explicit . a top-down approach is largely bottom-up, while a bottom-down one is mainly top-up . the authors argue that the goal is unclear and that the models are not scalable . |