Evaluating the Reliability and Interaction of Recursively Used
            Feature Classes for Terminology Extraction

Hätty, Anna; Dorna, Michael; Walde, Sabine Schulte im

doi:10.18653/v1/e17-4012

Cited by 8 publications

(4 citation statements)

References 18 publications

(11 reference statements)

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“…The categories Cosine, Domain and Head perform worst and do in most cases not even significantly improve over the baseline. The modifier features are better than the head features, which is in line with the results in (Hätty et al, 2017) where the modifier features are more important for detecting termhood than head features. For both the binary and the four-class tasks, the groups General, Compound and Contrastive perform best, with Compound as the winner for the binary task and Contrastive as the winner for the four-class task.…”

Section: Classification With Term and Compound Featuressupporting

confidence: 87%

“…Contrastive Selection via Heads (CSvH) (Basili et al, 2001) is a corporacomparing measure that computes termhood for a complex term by biasing the termhood score with the general-language frequency of the head. Hätty et al (2017) combine termhood measures within a random forest classifier to extract single and multiword terms and apply the measures recursively to the components. Hätty and Schulte im Walde (2018) demonstrate that propagating constituent information through neural networks improves the prediction of compound termhood.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains

Hätty¹,

Bettinger²,

Dorna³

et al. 2021

Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

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Predicting the difficulty of domain-specific vocabulary is an important task towards a better understanding of a domain, and to enhance the communication between lay people and experts. We investigate German closed noun compounds and focus on the interaction of compound-based lexical features (such as frequency and productivity) and terminologybased features (contrasting domain-specific and general language) across word representations and classifiers. Our prediction experiments complement insights from classification using (a) manually designed features to characterise termhood and compound formation and (b) compound and constituent word embeddings. We find that for a broad binary distinction into easy vs. difficult general-language compound frequency is sufficient, but for a more fine-grained four-class distinction it is crucial to include contrastive termhood features and compound and constituent features.

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Section: Classification With Term and Compound Featuressupporting

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains

Hätty¹,

Bettinger²,

Dorna³

et al. 2021

Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

Self Cite

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“…Statistical methods use statistical information, e.g., frequency of terms, to identify terms from a corpus (Frantzi et al, 2000;Nakagawa and Mori, 2002;Velardi et al, 2001;Drouin, 2003;Meijer et al, 2014). Machine learning methods learn a classifier, e.g., logistic regression classifier, with manually labeled data (Conrado et al, 2013;Fedorenko et al, 2014;Hätty et al, 2017). There also exists some work on automatic term extraction with Wikipedia (Vivaldi et al, 2012;Wu et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach

Huang

Chang

Xiong

et al. 2021

Preprint

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We propose to measure fine-grained domain relevancethe degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. Such measurement is crucial for many downstream tasks in natural language processing. To handle longtail terms, we build a core-anchored semantic graph, which uses core terms with rich description information to bridge the vast remaining fringe terms semantically. To support a finegrained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain. To reduce expensive human efforts, we employ automatic annotation and hierarchical positive-unlabeled learning. Our approach applies to big or small domains, covers head or tail terms, and requires little human effort. Extensive experiments demonstrate that our methods outperform strong baselines and even surpass professional human performance. 1

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“…None of the existing work satisfies all of them. Previously, research has been conducted on automatic keyword extraction (Hätty et al, 2017;Meng et al, 2017;Alzaidy et al, 2019;Wang et al, 2020) and phrase mining (Liu et al, 2015;Shang et al, 2018). However, their main focus is to extract terms from single/multiple documents without considering whether the extracted terms are distinctive to a target domain contrastive with a context.…”

Section: Introductionmentioning

confidence: 99%

Domain Representative Keywords Selection: A Probabilistic Approach

Akash¹,

Huang²,

Chang³

et al. 2022

Findings of the Association for Computational Linguistics: ACL 2022

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We propose a probabilistic approach to select a subset of a target domain representative keywords from a candidate set, contrasting with a context domain. Such a task is crucial for many downstream tasks in natural language processing. To contrast the target domain and the context domain, we adapt the two-component mixture model concept to generate a distribution of candidate keywords. It provides more importance to the distinctive keywords of the target domain than common keywords contrasting with the context domain. To support the representativeness of the selected keywords towards the target domain, we introduce an optimization algorithm for selecting the subset from the generated candidate distribution. We have shown that the optimization algorithm can be efficiently implemented with a near-optimal approximation guarantee. Finally, extensive experiments on multiple domains demonstrate the superiority of our approach over other baselines for the tasks of keyword summary generation and trending keywords selection. 1 * * This work was done while the author was at IBM Research, USA.

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Evaluating the Reliability and Interaction of Recursively Used Feature Classes for Terminology Extraction

Cited by 8 publications

References 18 publications

Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains

Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains

Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach

Domain Representative Keywords Selection: A Probabilistic Approach

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