Relation Mention Recognition Task
(Redirected from Semantic Relation Mention Recognition)
A relation mention recognition task is a relation recognition task that is a mention recognition task (requires the identification and classification of semantic relation mentions within a document set.
- AKA: RMR, Textual Relationship Recognition.
- input: a Text Corpus.
- output: The set of Semantic Relation Mentions from the Corpus.
- Performance Metrics:
- It can be solved by a Relation Mention Recognition System (that applies a Relation Mention Recognition algorithm).
- It can be supported by a Recognition Model Training Task.
- It can be decomposed into a Semantic Relation Mention Detection Task and a Semantic Relation Classification Task.
- It can range from being a Simple Relation Mention Recognition Task to being a Complex Relation Mention Recognition Task.
- It can range from being a Unary Relation Mention Recognition Task to being a Binary Relation Mention Recognition Task to being an N-ary Relation Mention Recognition Task.
- It can support: Relation Mention Annotation, Relation Mention Extraction, Question Answering, Information Retrieval, Resolution Tasks, ...
- a Subsumption Relation Mention Recognition Task, such as: RMR("A cat is a mammal.”) ⇒ TypeOf(cat, mammal). (of a subsumption relation mention)
- RMR("My cookie has chocolate chips.”) ⇒ Contains(chocolate chips, cookie). (see: meronymy relation, quantification)
- RMR("Alexander went to Australia.”) ⇒ RelocatedTo(Alexander, Australia).
- RMR("Microsoft is based in Redmond.”) ⇒ CompanyHeadquarterLocation(Microsoft, Redmond). (see: domain specific relation)
- RMR("Albert's niece Ann got engaged to John.”) DaughterOfSibling(Albert,Ann) ^ Engaged(Ann, John). (see: appositive relation).
- RMR("The expression of mouse p53 inhibits simian virus 40 replication.”) ⇒ OrganismComponent(mouse, p53). An Organism Component Semantic Relation Recognition Task.
- RMR("XyaA is one of E. coli’s proteins. It is found in the periplasmic space.”) ⇒ SubcellularLocalization(E. coli, XyaA, periplasmic space), a Subcellular Localization Relation Recognition Task.
- RMR("Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1 homolog) directly phosphorylated Swe1 and this modification served as a priming step to promote subsequent Cdc5-dependent Swe1 hyperphosphorylation and degradation.”) ⇒ Complex: Clb2–Cdc28; and Phosphorylation: Clb2=>Swe1, Cdc28=>Swe1, and Cdc5=>Swe1, a Protein-Protein Interaction Recognition Task.
- RMR("He wouldn't accept anything of value from those he was writing about.”) ⇒
[A0 He] [AM-MOD would] [AM-NEG n't] [V accept] [A1 anything of value] from [A2 those he was writing about] ., a Semantic Role Labeling Task.
- HeadquarterLocation(Organization, Location) (Snowball)
- SubcellularProteinLocalization(Organism, Protein, Location) (PPLRE Project).
- a Semantic Relation Mention Recognition Benchmark Task.
- See: Information Extraction Task, Word Sense Disambiguation Task.
- (Sarawagi, 2008) ⇒ Sunita Sarawagi. (2008). “Information extraction.” In: FnT Databases, 1(3).
- The problem of relationship extraction has been studied extensively on natural language text, including news articles , scientific publications , Blogs, emails , and sources like Wikipedia [196, 197] and the general web [4, 14].
- (Girju et al., 2007) ⇒ Roxana Girju, Preslav Nakov, Vivi Nastase, Stan Szpakowicz, Peter D. Turney, and Deniz Yuret. (2007). “SemEval-2007 Task 04: Classification of Semantic Relations between Nominals.” In: Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval 2007).
- (McCallum, 2006) ⇒ Andrew McCallum. (2006). “Information Extraction, Data Mining and Joint Inference. SIGKDD Proceedings (KDD-2006). (paper.pdf)
- (Culotta et al., 2006) ⇒ Aron Culotta, Andrew McCallum, and Jonathan Betz. (2006). “Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text.” In: Proceedings of HLT-NAACL 2006.
- (Bizer et al., 2005) ⇒ Christian Bizer, Ralf Heese, Malgorzata Mochol, Radoslaw Oldakowski, Robert Tolksdorf, and Rainer Eckstein. (2005). “The Impact of Semantic Web Technologies on Job Recruitment Processes.” 7. Internationale Tagung Wirtschaftsinformatik (WI 2005).
- (Culotta & Sorensen, 2004) ⇒ Aron Culotta, and Jeffrey S. Sorensen. (2004). “Dependency Tree Kernels for Relation Extraction.” In: Proceedings of ACL Conference (ACL 2004).
- Mihai Surdeanu, Sanda M. Harabagiu, J. Williams and P. Aarseth. (2003). Using Predicate-Argument Structures for Information Extraction. In: Proceedings of Assoc. for Computational Linguistics (ACL). http://acl.ldc.upenn.edu/acl2003/main/pdfs/Surdeanu.pdf
- Induce predicate-argument structures from parse trees with simple rules that map predicate arguments to domain-specific template slots.
- (Roth and Yih, 2002) ⇒ Dan Roth and W. Yih. (2002). “Probabilistic Reasoning for Entity & Relation Recognition.” In: the 20th International Conference on Computational Linguistics (COLING-2002). paper.pdf
- (Laender et al., 2002) ⇒ Alberto H. F. Laender, Berthier A. Ribeiro-Neto, Altigran S. da Silva, and Juliana S. Teixeira. (2002). “A Brief Survey of Web Data Extraction Tools.” In: ACM SIGMOD Record, 31(2). doi:10.1145/565117.565137
- Fabio Ciravegna. (2001). Adaptive information extraction from text by rule induction and generalization. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI 2001).
- (Park, 2001) ⇒ J. C. Park. (2001). Using Combinatory Categorical Grammar to Extract Biomedical Information. In: IEEE Intelligent Systems.
- applies parsing for automatic database curation from biomedical research papers.
- (Agichtein and Gravano, 2000) ⇒ Eugene Agichtein and L. Gravano. (2000). “Snowball: Extracting Relations from Large Plain-Text Collections.” In: Proceedings of the 5th ACM International Conference on Digital Libraries (DL-2000). (tech report.pdf)
- (Miller et al., 2000) ⇒ Scott Miller, Heidi Fox, Lance Ramshaw, and Ralph Weischedel. (2000). “A Novel Use of Statistical Parsing to Extract Information from Text.” In: Proceedings of NAACL Conference (NAACL 2000).
- Add simple entity and relation annotations on top of syntax, and train a parser to recover both in parallel. Finished second in MUC-7.
- (Nahm & Mooney, 2000) ⇒ U. Y. Nahm, and Raymond Mooney. (2000). “A Mutually Beneficial Integration of Data Mining and Information Extraction.” In: Proceedings of the Seventeenth National Conference on ArtificialIntelligence (AAAI-2000).
- This paper describes a system called DiscoTEX, that combines IE and data mining methodologies to perform text mining as well as improve the performance of the underlying extraction system. Rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of IE. Encouraging results are presented on applying these techniques to a corpus of computer job postings from an Internet newsgroup.
- (Cohen et al., 2000) ⇒ William W. Cohen, Andrew McCallum, and D. Quass. (2000). “Learning to Understand the Web.” In: Bulletin of the IEEE Computer Society Technical Committee on Data Engineering.
- "Information Extraction tasks are characterized by two properties: the desired knowledge can be relatively simple and fixed templated, or frame, with slots that need to be filled in with material from the text, and only a small part of the information in the text is relevant for fillin in its frame”
- Colins & Singer. (1999). “Unsupervised models for named entity classification.”
- Dayne Freitag. (1998). Information Extraction from HTML: Application of a general learning approach. Proceedings of the Fifteenth Conference on Artificial Intelligence AAAI-98. http://citeseer.ist.psu.edu/freitag98information.html
- (Giles et al., 1998) ⇒ C. Lee Giles, Kurt D. Bollacker, and Steve Lawrence. (1998). “CiteSeer: An automatic Citation Indexing System.” In: The Third ACM Conference on Digital Libraries (1998).
- M. Craven, D. DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, K. Nigam, and S. Slattery. (1998). Learning to extract symbolic knowledge from the world wide web. In: Proceedings of AAAI-98.
- (Khoo, 1997) ⇒ C. Khoo. 1997. The Use of Relation Matching in Information Retrieval. LIBRES: Library and Information Science Research Electronic Journal, 7(2). (paper.html)
- N. Kushmerick, D. Weld and R. Doorenbos. (1997). “Wrapper induction for information extraction.” In: IJCAI (1997).
- T. R. Leek. (1997). Information Extraction using Hidden Markov Models. Master's thesis, UC San Diego. http://citeseer.ist.psu.edu/leek97information.html
- S. Soderland 1997 learning to extract Text Based information from the World Wide Web
- S. Soderland, D. Fisher, J. Aseltine, and W. Lehnert. (1995). Crystal: Inducing a Conceptual Dictionary. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. http://citeseer.ist.psu.edu/soderland95crystal.html
- (Hearst, 1992) ⇒ Marti Hearst. (1992). “Automatic Acquisition of Hyponyms from Large Text Corpora.” In: Proceedings of the 14th International Conference on Computational Linguistics (COLING 1992).
- L. Rau. (1991). “Extracting Company Names From Text.” In: Proceedings of the Sixth Conference on Artificial Intelligence Applications.
IE Task Open Issues
- Integration of IE and TM [2003_ANoteOnUnifying...]
- Allow for patterns to refer to generalized words. E.g. "to recognize as" <=> "to know as" by WordNet relationship (validate this example)
- Weak theoretical underpinnings
- The extraction of Long-Distance Dependency (LDD) and the mapping to shallow semantic representations is not always possible from the output of Syntactic Parsers.
- Generic: InstanceOf(entity, class), IsA(subclass, class), PartOf(part, thing),
- Specific: Cities(x), Elements(x), HeadquarterLocation(organization, location), DateOfBirth(person, date), Person(x)
IE Task Models, Summary
- Text Surface Pattern
- Hearst "x such as y"
- Snowball: [left words, EntityType1, middle words, <w> <w>.
- Lexico-Syntactic Pattern
IE Task Evaluation Metrics
- An IE system is typically evaluated in terms of:
# of correct answers biven by the system / total # of answers given
# of correct answers given by the system/
total # of possible correct answers in the text
# of incorrect answers given by the system / # of spurious facts in the text
- F-measure: ...