Konferenzbeitrag
Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.
- Language
-
Englisch
Subject
Computerlinguistik
Event
Geistige Schöpfung
(who)
Wiegand, Michael
Roth, Benjamin
Klakow, Dietrich
Roth, Benjamin
Klakow, Dietrich
(when)
2019-02-05
Event
Veröffentlichung
(who)
Stroudsburg, PA : Association for Computational Linguistics
- URN
-
urn:nbn:de:bsz:mh39-84696
- Last update
-
14.09.2023, 8:26 AM CEST
Object type
- Konferenzbeitrag
Associated
- Wiegand, Michael
- Roth, Benjamin
- Klakow, Dietrich
- Stroudsburg, PA : Association for Computational Linguistics
Time of origin
- 2019-02-05
Other Objects (12)
Data-driven Knowledge Extraction for the Food Domain
Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction
Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives
Predicative Adjectives: An Unsupervised Criterion to Extract Subjective Adjectives
Towards the Detection of Reliable Food-Health Relationships
Hybrid Approaches for Sentiment Analysis
Separating Brands from Types: an Investigation of Different Features for the Food Domain
Towards Contextual Healthiness Classification of Food Items - A Linguistic Approach
Opinion Holder and Target Extraction based on the Induction of Verbal Categories
Detecting conditional healthiness of food items from natural language text
Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification
Towards the Detection of Reliable Food-Health Relationships
Data-driven Knowledge Extraction for the Food Domain
Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction
Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives
Predicative Adjectives: An Unsupervised Criterion to Extract Subjective Adjectives
Towards the Detection of Reliable Food-Health Relationships
Hybrid Approaches for Sentiment Analysis
Separating Brands from Types: an Investigation of Different Features for the Food Domain
Towards Contextual Healthiness Classification of Food Items - A Linguistic Approach
Opinion Holder and Target Extraction based on the Induction of Verbal Categories
Detecting conditional healthiness of food items from natural language text
Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification
Towards the Detection of Reliable Food-Health Relationships
Data-driven Knowledge Extraction for the Food Domain
Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction
Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives
Predicative Adjectives: An Unsupervised Criterion to Extract Subjective Adjectives
Towards the Detection of Reliable Food-Health Relationships
Hybrid Approaches for Sentiment Analysis
Separating Brands from Types: an Investigation of Different Features for the Food Domain
Towards Contextual Healthiness Classification of Food Items - A Linguistic Approach
Opinion Holder and Target Extraction based on the Induction of Verbal Categories
Detecting conditional healthiness of food items from natural language text
Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification