Unsupervised Semantic Frame Induction using Triclustering

Unsupervised Semantic Frame Induction using Triclustering

Dmitry Ustalov University of Mannheim, Germany Alexander Panchenko University of Hamburg, Germany Andrei Kutuzov University of Oslo, Norway
Chris Biemann
University of Hamburg, Germany
Simone Paolo Ponzetto University of Mannheim, Germany

We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction. We cast the frame induction problem as a triclustering problem that is a generalization of clustering for triadic data. Our replicable benchmarks demonstrate that the proposed graph-based approach, Triframes, shows state-of-the art results on this task on a FrameNet-derived dataset and performing on par with competitive methods on a verb class clustering task.

Unsupervised Semantic Frame Induction using Triclustering

1 Introduction

Recent years have seen much work on Frame Semantics (Fillmore, 1982), enabled by the availability of a large set of frame definitions, as well as a manually annotated text corpus provided by the FrameNet project (Baker et al., 1998). FrameNet data enabled the development of wide-coverage frame parsers using supervised learning (Gildea and Jurafsky, 2002; Erk and Padó, 2006; Das et al., 2014, inter alia), as well as its application to a wide range of tasks, ranging from answer extraction in Question Answering (Shen and Lapata, 2007) and Textual Entailment (Burchardt et al., 2009; Ben Aharon et al., 2010).

However, frame-semantic resources are arguably expensive and time-consuming to build due to difficulties in defining the frames, their granularity and domain, as well as the complexity of the construction and annotation tasks requiring expertise in the underlying knowledge. Consequently, such resources exist only for a few languages (Boas, 2009) and even English is lacking domain-specific frame-based resources. Possible inroads are cross-lingual semantic annotation transfer (Padó and Lapata, 2009; Hartmann et al., 2016) or linking FrameNet to other lexical-semantic or ontological resources (Narayanan et al., 2003; Tonelli and Pighin, 2009; Laparra and Rigau, 2010; Gurevych et al., 2012, inter alia). But while the arguably simpler task of PropBank-based Semantic Role Labeling has been successfully addressed by unsupervised approaches (Lang and Lapata, 2010; Titov and Klementiev, 2011), fully unsupervised frame-based semantic annotation exhibits far more challenges, starting with the preliminary step of automatically inducing a set of semantic frame definitions that would drive a subsequent text annotation. In this work, we aim at overcoming these issues by automatizing the process of FrameNet construction through unsupervised frame induction techniques.

FrameNet Role Lexical Units (LU)
Perpetrator Subject kidnapper, alien, militant
FEE Verb snatch, kidnap, abduct
Victim Object son, people, soldier, child
Table 1: Example of a LU tricluster corresponding to the “Kidnapping” frame from FrameNet.


In this work, we cast the frame induction problem as a triclustering task (Zhao and Zaki, 2005; Ignatov et al., 2015), namely a generalization of standard clustering and bi-clustering (Cheng and Church, 2000), aiming at simultaneously clustering objects along three dimensions (cf. Table 1). First, using triclustering allows to avoid sequential nature of frame induction approaches, e.g. (Kawahara et al., 2014), where two independent clusterings are needed. Second, benchmarking frame induction as triclustering against other methods on dependency triples allows to abstract away the evaluation of the frame induction algorithm from other factors, e.g., the input corpus or pre-processing steps, thus allowing a fair comparison of different induction models.

The contributions of this paper are three-fold: (1) we are the first to apply triclustering algorithms for unsupervised frame induction, (2) we propose a new approach to triclustering, achieving state-of-the-art performance on the frame induction task, (3) we propose a new method for the evaluation of frame induction enabling straightforward comparison of approaches. In this paper, we focus on the simplest setup with subject-verb-object (SVO) triples and two roles, but our evaluation framework can be extended to more roles.

In contrast to the recent approaches like the one by Jauhar and Hovy (2017), our approach induces semantic frames without any supervision, yet capturing only two core roles: the subject and the object of a frame triggered by verbal predicates. Note that it is not generally correct to expect that the SVO triples obtained by a dependency parser are necessarily the core arguments of a predicate. Such roles can be implicit, i.e., unexpressed in a given context (Schenk and Chiarcos, 2016). Keeping this limitation in mind, we assume that the triples obtained from a Web-scale corpus cover most core arguments sufficiently.

Related Work.

LDA-Frames (Materna, 2012, 2013) is an approach to inducing semantic frames using LDA (Blei et al., 2003) for generating semantic frames and their respective frame-specific semantic roles at the same time. The authors evaluated their approach against the CPA corpus (Hanks and Pustejovsky, 2005). ProFinder (Cheung et al., 2013) is another generative approach that also models both frames and roles as latent topics. The evaluation was performed on the in-domain information extraction task MUC-4 (Sundheim, 1992) and on the text summarization task TAC-2010.111https://tac.nist.gov/2010/Summarization Modi et al. (2012) build on top of an unsupervised semantic role labeling model (Titov and Klementiev, 2012). The raw text of sentences from the FrameNet data is used for training. The FrameNet gold annotations are then used to evaluate the labeling of the obtained frames and roles, effectively clustering instances known during induction. Kawahara et al. (2014) harvest a huge collection of verbal predicates along with their argument instances and then apply the Chinese Restaurant Process clustering algorithm to group predicates with similar arguments. The approach was evaluated on the verb cluster dataset of Korhonen et al. (2003).

A major issue with unsupervised frame induction task is that these and some other related approaches, e.g., (O’Connor, 2013), were all evaluated in completely different incomparable settings, and used different input corpora. In this paper, we propose a methodology to resolve this issue.

2 The Triframes Algorithm

Our approach to frame induction relies on graph clustering. We focused on a simple setup using two roles and the SVO triples, arguing that it still can be useful, as frame roles are primarily expressed by subjects and objects, giving rise to semantic structures extracted in an unsupervised way with high coverage.

Input Data.

As the input data, we use SVO triples extracted by a dependency parser. According to our statistics on the dependency-parsed FrameNet corpus of over 150 thousand sentences (Bauer et al., 2012), the SUBJ and OBJ relationships are the two most common shortest paths between frame evoking elements (FEEs) and their roles, accounting for 13.5 % of instances of a heavy-tail distribution of over 11 thousand different paths that occur three times or more in the FrameNet data. While this might seem a simplification that does not cover prepositional phrases and frames filling the roles of other frames in a nested fashion, we argue that the overall frame inventory can be induced on the basis of this restricted set of constructions, leaving other paths and more complex instances for further work.

The Method.

Our method constructs embeddings for SVO triples to reduce the frame induction problem to a simpler graph clustering problem. Given the vocabulary , a -dimensional word embedding model , and a set of SVO triples extracted from a syntactically analyzed corpus, we construct the triple similarity graph . Clustering of yields sets of triples corresponding to the instances of the semantic frames, thereby clustering frame-evoking predicates and roles simultaneously.

We obtain dense representations of the triples by concatenating the word vectors corresponding to the elements of each triple by transforming a triple into the -dimensional vector . Subsequently, we use the triple embeddings to generate the undirected graph by constructing the edge set . For that, we compute nearest neighbors of each triple vector and establish cosine similarity-weighted edges between the corresponding triples.

Then, we assume that the triples representing similar contexts appear in similar roles, which is explicitly encoded by the concatenation of the corresponding vectors of the words constituting the triple. We use graph clustering of to retrieve communities of similar triples forming frame clusters; a clustering algorithm is a function such that . Finally, for each cluster , we aggregate the subjects, the verbs, and the objects of the contained triples into separate sets. As the result, each cluster is transformed into a triframe, which is a triple that is composed of the subjects , the verbs , and the objects .

0:  an embedding model ,
0:  a set of SVO triples ,
0:  the number of nearest neighbors ,
0:  a graph clustering algorithm Cluster.
0:  a set of triframes .
4:  for all  do
9:  return  
Algorithm 1 Triframes frame induction

Our frame induction approach outputs a set of triframes as presented in Algorithm 1. The hyper-parameters of the algorithm are the number of nearest neighbors for establishing edges () and the graph clustering algorithm Cluster. During the concatenation of the vectors for words forming triples, the -dimensional vector space is created. Thus, given the triple , we denote the nearest neighbors extraction procedure of its concatenated embedding from as . We used nearest neighbors per triple.

To cluster the nearest neighbor graph of SVO triples , we use the Watset fuzzy graph clustering algorithm (Ustalov et al., 2017). It treats the vertices of the input graph as the SVO triples, induces their senses, and constructs an intermediate sense-aware representation that is clustered using the Chinese Whispers (CW) hard clustering algorithm (Biemann, 2006). We chose Watset due to its performance on the related synset induction task, its fuzzy nature, and the ability to find the number of frames automatically.

3 Evaluation

Input Corpus.

In our evaluation, we use triple frequencies from the DepCC dataset (Panchenko et al., 2018) , which is a dependency-parsed version of the Common Crawl corpus, and the standard 300-dimensional word embeddings model trained on the Google News corpus (Mikolov et al., 2013). All evaluated algorithms are executed on the same set of triples, eliminating variations due to different corpora or pre-processing.


We cast the complex multi-stage frame induction task as a straightforward triple clustering task. We constructed a gold standard set of triclusters, each corresponding to a FrameNet frame, similarly to the one illustrated in Table 1. To construct the evaluation dataset, we extracted frame annotations from the over 150 thousand sentences from the FrameNet 1.7 (Baker et al., 1998). Each sentence contains data about the frame, FEE, and its arguments, which were used to generate triples in the form , where correspond to the roles and FEE in the sentence. We omitted roles expressed by multiple words as we use dependency parses, where one node represents a single word only.

For the sentences where more than two roles are present, all possible triples were generated. Sentences with less than two roles were omitted. Finally, for each frame, we selected only two roles, which are most frequently co-occurring in the FrameNet annotated texts. This has left us with about 100 thousand instances for the evaluation. For the evaluation purposes, we operate on the intersection of triples from DepCC and FrameNet. Experimenting on the full set of DepCC triples is only possible for several methods that scale well (Watset, CW, -means), but is prohibitively expensive for other methods (LDA-Frames, NOAC).

In addition to the frame induction evaluation, where subjects, objects, and verbs are evaluated together, we also used a dataset of polysemous verb classes introduced in (Korhonen et al., 2003) and employed by Kawahara et al. (2014). Statistics of both datasets are summarized in Table 2. Note that the polysemous verb dataset is rather small, whereas the FrameNet triples set is fairly large, enabling reliable comparisons.

Dataset # instances # unique # clusters
FrameNet Triples 99,744 94,170 383
Poly. Verb Classes 246 110 62
Table 2: Statistics of the evaluation datasets.

Evaluation Measures.

Following the approach for verb class evaluation by Kawahara et al. (2014), we employ normalized modified purity () and normalized inverse purity () as the clustering quality measures. Given the set of the obtained clusters and the set of the gold clusters , normalized modified purity quantifies the clustering precision as the average of the weighted overlap between each cluster and the gold cluster that maximizes the overlap with : , where the weighted overlap is the sum of the weights for each word in -th cluster: . Note that counts all the singleton clusters as wrong. Similarly, normalized inverse purity (collocation) quantifies the clustering recall: . and are combined together as the harmonic mean to yield the overall clustering F-score (), which we use to rank the approaches.

Our framework can be extended to evaluation of more than two roles by generating more roles per frame. Currently, given a set of gold triples generated from the FrameNet, each triple element has a role, e.g., “Victim”, “Predator”, and “FEE”. We use fuzzy clustering evaluation measure which operates not on triples, but instead on a set of tuples. Consider for instance a gold triple . It will be converted to three pairs , , . Each cluster in both and is transformed into a union of all constituent typed pairs. The quality measures are finally calculated between these two sets of tuples, , and . Note that one can easily pull in more than two core roles by adding to this gold standard set of tuples other roles of the frame, e.g., . In our experiments, we focused on two main roles as our contribution is related to the application of triclustering methods. However, if more advanced methods of clustering are used, yielding clusters of arbitrary modality (-clustering), one could also use our evaluation schema.


We compare our method to several available state-of-the-art baselines applicable to our dataset of triples.

LDA-Frames by Materna (2012, 2013) is a frame induction method based on topic modeling. We ran 500 iterations of the model with the default parameters. Higher-Order Skip-Gram (HOSG) by Cotterell et al. (2017) generalizes the Skip-Gram model (Mikolov et al., 2013) by extending it from word-context co-occurrence matrices to tensors factorized with a polyadic decomposition. In our case, this tensor consisted of SVO triple counts. We trained three vector arrays (for subjects, verbs and objects) on the 108,073 SVO triples from the FrameNet corpus, using the implementation by the authors. Training was performed with 5 negative samples, 300-dimensional vectors, and 10 epochs. We constructed an embedding of a triple by concatenating embeddings for subjects, verbs, and objects, and clustered them using -means with the number of clusters set to 10,000 (this value provided the best performance). NOAC (Egurnov et al., 2017) is an extension of the Object Attribute Condition (OAC) triclustering algorithm (Ignatov et al., 2015) to numerically weighted triples. This incremental algorithm searches for dense regions in triadic data. A minimum density of 0.25 led to the best results. In the Triadic baselines, independent word embeddings of subject, object, and verb are concatenated and then clustered using a hard clustering algorithm: -means, spectral clustering, or CW.

We tested various hyper-parameters of each of these algorithms and report the best results overall per clustering algorithm. Two trivial baselines are Singletons that creates a single cluster per instance and Whole that creates one cluster for all elements.

Verb Subject Object Frame
Method nmPU niPU F1 nmPU niPU F1 nmPU niPU F1 nmPU niPU F1
Triframes Watset 42.84 88.35 57.70 54.22 81.40 65.09 53.04 83.25 64.80 55.19 60.81 57.87
HOSG (Cotterell et al., 2017) 44.41 68.43 53.86 52.84 74.53 61.83 54.73 74.05 62.94 55.74 50.45 52.96
NOAC (Egurnov et al., 2017) 20.73 88.38 33.58 57.00 80.11 66.61 57.32 81.13 67.18 44.01 63.21 51.89
Triadic Spectral 49.62 24.90 33.15 50.07 41.07 45.13 50.50 41.82 45.75 52.05 28.60 36.91
Triadic -Means 63.87 23.16 33.99 63.15 38.20 47.60 63.98 37.43 47.23 63.64 24.11 34.97
LDA-Frames (Materna, 2013) 26.11 66.92 37.56 17.28 83.26 28.62 20.80 90.33 33.81 18.80 71.17 29.75
Triframes CW 7.75 6.48 7.06 3.70 14.07 5.86 51.91 76.92 61.99 21.67 26.50 23.84
Singletons 0.00 25.23 0.00 0.00 25.68 0.00 0.00 20.80 0.00 32.34 22.15 26.29
Whole 3.62 100.0 6.98 2.41 98.41 4.70 2.38 100.0 4.64 2.63 99.55 5.12
Table 3: Frame evaluation results on the triples from the FrameNet 1.7 corpus (Baker et al., 1998). The results are sorted by the descending order of the Frame F1-score. Best results are boldfaced.

4 Results

We perform two experiments to evaluate our approach: (1) a frame induction experiment on the FrameNet annotated corpus by Bauer et al. (2012); (2) the polysemous verb clustering experiment on the dataset by Korhonen et al. (2003). The first is based on the newly introduced frame induction evaluation schema (cf. Section 3). The second one evaluates the quality of verb clusters only on a standard dataset from prior work.

Frame Induction Experiment.

In Table 3 and Figure 1, the results of the experiment are presented. Triframes based on Watset clustering outperformed the other methods on both Verb F1 and overall Frame F1. The HOSG-based clustering proved to be the most competitive baseline, yielding decent scores according to all four measures. The NOAC approach captured the frame grouping of slot fillers well but failed to establish good verb clusters. Note that NOAC and HOSG use only the graph of syntactic triples and do not rely on pre-trained word embeddings. This suggests a high complementarity of signals based on distributional similarity and global structure of the triple graph. Finally, the simpler Triadic baselines relying on hard clustering algorithms showed low performance, similar to that of LDA-Frames, justifying the more elaborate Watset method.

While triples are intuitively less ambiguous than words, still some frequent and generic triples like can act as hubs in the graph, making it difficult to split it into semantically plausible clusters. The poor results of the Chinese Whispers hard clustering algorithm illustrate this. Since the hubs are ambiguous, i.e., can belong to multiple clusters, the use of the Watset fuzzy clustering algorithm that splits the hubs by disambiguating them leads to the best results (see Table 3).

Figure 1: F1-scores for   verbs,   subjects,   objects,   frames corresponding to Table 3.

Verb Clustering Experiment.

Table 4 presents results on the second dataset for the best models identified on the first dataset. The LDA-Frames yielded the best results with our approach performing comparably in terms of the F1-score. We attribute the low performance of the Triframes method based on CW clustering to its hard partitioning output, whereas the evaluation dataset contains fuzzy clusters. Different rankings also suggest that frame induction cannot simply be treated as a verb clustering and requires a separate task.

Method nmPU niPU F1
LDA-Frames 52.60 45.84 48.98
Triframes Watset 40.05 62.09 48.69
NOAC 37.19 64.09 47.07
HOSG 38.22 43.76 40.80
Triadic Spectral 35.76 38.96 36.86
Triadic -Means 52.22 27.43 35.96
Triframes CW 18.05 12.72 14.92
Whole 24.14 79.09 36.99
Singletons 0.00 27.21 0.00
Table 4: Evaluation results on the dataset of polysemous verb classes by Korhonen et al. (2003).

5 Conclusion

In this paper, we presented the first application of triclustering for unsupervised frame induction. We designed a dataset based on the FrameNet and SVO triples to enable fair corpus-independent evaluations of frame induction algorithms. We tested several triclustering methods as the baselines and proposed a new graph-based triclustering algorithm that yields state-of-the-art results. A promising direction for future work is using the induced frames in applications, such as Information Extraction and Question Answering.

Additional illustrations and examples of extracted frames are available in the supplementary materials. The source code and the data are available online under a permissive license.222https://github.com/uhh-lt/triframes


We acknowledge the support of DFG under the “JOIN-T” and “ACQuA” projects and thank three anonymous reviewers for their helpful comments. Furthermore, we thank Dmitry Ignatov and Dmitry Gnatyshak for help in operating the NOAC method using the multimodal clustering toolbox. Besides, we are grateful to Ryan Cotterell and Adam Poliak for a discussion and an implementation of the HOSG method. Finally, we thank Bonaventura Coppolla for discussions and preliminary work on graph-based frame induction.


  • Baker et al. (1998) Collin F. Baker, Charles J. Fillmore, and John B. Lowe. 1998. The Berkeley FrameNet Project. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 1, ACL ’98, pages 86–90, Montreal, QC, Canada. Association for Computational Linguistics.
  • Bauer et al. (2012) Daniel Bauer, Hagen Fürstenau, and Owen Rambow. 2012. The Dependency-Parsed FrameNet Corpus. In Proceedings of the Eight International Conference on Language Resources and Evaluation, LREC 2012, pages 3861–3867, Istanbul, Turkey. European Language Resources Association (ELRA).
  • Ben Aharon et al. (2010) Roni Ben Aharon, Idan Szpektor, and Ido Dagan. 2010. Generating Entailment Rules from FrameNet. In Proceedings of the ACL 2010 Conference Short Papers, pages 241–246, Uppsala, Sweden. Association for Computational Linguistics.
  • Biemann (2006) Chris Biemann. 2006. Chinese Whispers: An Efficient Graph Clustering Algorithm and Its Application to Natural Language Processing Problems. In Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, TextGraphs-1, pages 73–80, New York, NY, USA. Association for Computational Linguistics.
  • Blei et al. (2003) David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993–1022.
  • Boas (2009) Hans C. Boas. 2009. Multilingual FrameNets in Computational Lexicography: Methods and Applications. Trends in Linguistics. Studies and Monographs. Mouton de Gruyter.
  • Burchardt et al. (2009) Aljoscha Burchardt, Marco Pennacchiotti, Stefan Thater, and Manfred Pinkal. 2009. Assessing the impact of frame semantics on textual entailment. Natural Language Engineering, 15(4):527–550.
  • Cheng and Church (2000) Yizong Cheng and George M. Church. 2000. Biclustering of Expression Data. In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pages 93–103. AAAI Press.
  • Cheung et al. (2013) Jackie C. K. Cheung, Hoifung Poon, and Lucy Vanderwende. 2013. Probabilistic Frame Induction. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 837–846, Atlanta, GA, USA. Association for Computational Linguistics.
  • Cotterell et al. (2017) Ryan Cotterell, Adam Poliak, Benjamin Van Durme, and Jason Eisner. 2017. Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 175–181, Valencia, Spain. Association for Computational Linguistics.
  • Das et al. (2014) Dipanjan Das, Desai Chen, André F. T. Martins, Nathan Schneider, and Noah A. Smith. 2014. Frame-Semantic Parsing. Computational Linguistics, 40(1):9–56.
  • Egurnov et al. (2017) Dmitry Egurnov, Dmitry Ignatov, and Engelbert M. Nguifo. 2017. Mining Triclusters of Similar Values in Triadic Real-Valued Contexts. In 14th International Conference on Formal Concept Analysis - Supplementary Proceedings, pages 31–47, Rennes, France.
  • Erk and Padó (2006) Katrin Erk and Sebastian Padó. 2006. Shalmaneser — A Toolchain For Shallow Semantic Parsing. In Proceedings of the Fifth International Conference on Language Resources and Evaluation, LREC 2006, pages 527–532, Genoa, Italy. European Language Resources Association (ELRA).
  • Fillmore (1982) Charles J. Fillmore. 1982. Frame Semantics. In Linguistics in the Morning Calm, pages 111–137. Hanshin Publishing Co., Seoul, South Korea.
  • Gildea and Jurafsky (2002) Daniel Gildea and Martin Jurafsky. 2002. Automatic Labeling of Semantic Roles. Computational Linguistics, 28(3):245–288.
  • Gurevych et al. (2012) Iryna Gurevych, Judith Eckle-Kohler, Silvana Hartmann, Michael Matuschek, Christian M. Meyer, and Christian Wirth. 2012. UBY – A Large-Scale Unified Lexical-Semantic Resource Based on LMF. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, EACL ’12, pages 580–590, Avignon, France. Association for Computational Linguistics.
  • Hanks and Pustejovsky (2005) Patrick Hanks and James Pustejovsky. 2005. A Pattern Dictionary for Natural Language Processing. Revue Française de linguistique appliquée, 10(2):63–82.
  • Hartmann et al. (2016) Silvana Hartmann, Judith Eckle-Kohler, and Iryna Gurevych. 2016. Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources. Transactions of the Association for Computational Linguistics, 4:197–213.
  • Ignatov et al. (2015) Dmitry I. Ignatov, Dmitry V. Gnatyshak, Sergei O. Kuznetsov, and Boris G. Mirkin. 2015. Triadic Formal Concept Analysis and triclustering: searching for optimal patterns. Machine Learning, 101(1-3):271–302.
  • Jauhar and Hovy (2017) Sujay Kumar Jauhar and Eduard Hovy. 2017. Embedded Semantic Lexicon Induction with Joint Global and Local Optimization. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 209–219, Vancouver, Canada. Association for Computational Linguistics.
  • Kawahara et al. (2014) Daisuke Kawahara, Daniel W. Peterson, and Martha Palmer. 2014. A Step-wise Usage-based Method for Inducing Polysemy-aware Verb Classes. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, ACL 2014, pages 1030–1040, Baltimore, MD, USA. Association for Computational Linguistics.
  • Korhonen et al. (2003) Anna Korhonen, Yuval Krymolowski, and Zvika Marx. 2003. Clustering Polysemic Subcategorization Frame Distributions Semantically. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1, ACL ’03, pages 64–71, Sapporo, Japan. Association for Computational Linguistics.
  • Lang and Lapata (2010) Joel Lang and Mirella Lapata. 2010. Unsupervised Induction of Semantic Roles. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 939–947, Los Angeles, CA, USA. Association for Computational Linguistics.
  • Laparra and Rigau (2010) Egoitz Laparra and German Rigau. 2010. eXtended WordFrameNet. In Proceedings of the Seventh International Conference on Language Resources and Evaluation, LREC 2010, pages 1214–1219, Valletta, Malta. European Language Resources Association (ELRA).
  • Materna (2012) Jiří Materna. 2012. LDA-Frames: An Unsupervised Approach to Generating Semantic Frames. In Computational Linguistics and Intelligent Text Processing, Proceedings, Part I, CICLing 2012, pages 376–387, New Delhi, India. Springer Berlin Heidelberg.
  • Materna (2013) Jiří Materna. 2013. Parameter Estimation for LDA-Frames. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 482–486, Atlanta, GA, USA. Association for Computational Linguistics.
  • Mikolov et al. (2013) Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems 26, pages 3111–3119. Curran Associates, Inc., Harrahs and Harveys, NV, USA.
  • Modi et al. (2012) Ashutosh Modi, Ivan Titov, and Alexandre Klementiev. 2012. Unsupervised Induction of Frame-Semantic Representations. In Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure, pages 1–7, Montréal, Canada. Association for Computational Linguistics.
  • Narayanan et al. (2003) Srini Narayanan, Collin Baker, Charles Fillmore, and Miriam Petruck. 2003. FrameNet Meets the Semantic Web: Lexical Semantics for the Web. In The Semantic Web - ISWC 2003: Second International Semantic Web Conference, Sanibel Island, FL, USA, October 20-23, 2003. Proceedings, pages 771–787, Heidelberg, Germany. Springer Berlin Heidelberg.
  • O’Connor (2013) Brendan O’Connor. 2013. Learning Frames from Text with an Unsupervised Latent Variable Model. arXiv preprint arXiv:1307.7382.
  • Padó and Lapata (2009) Sebastian Padó and Mirella Lapata. 2009. Cross-lingual Annotation Projection of Semantic Roles. Journal of Artificial Intelligence Research, 36(1):307–340.
  • Panchenko et al. (2018) Alexander Panchenko, Eugen Ruppert, Stefano Faralli, Simone Paolo Ponzetto, and Chris Biemann. 2018. Building a Web-Scale Dependency-Parsed Corpus from Common Crawl. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, pages 1816–1823, Miyazaki, Japan. European Language Resources Association (ELRA).
  • Schenk and Chiarcos (2016) Niko Schenk and Christian Chiarcos. 2016. Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1473–1479, San Diego, CA, USA. Association for Computational Linguistics.
  • Shen and Lapata (2007) Dan Shen and Mirella Lapata. 2007. Using Semantic Roles to Improve Question Answering. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 12–21, Prague, Czech Republic. Association for Computational Linguistics.
  • Sundheim (1992) Beth M. Sundheim. 1992. Overview of the Fourth Message Understanding Evaluation and Conference. In Proceedings of the 4th Conference on Message Understanding, MUC4 ’92, pages 3–21, Stroudsburg, PA, USA. Association for Computational Linguistics.
  • Titov and Klementiev (2011) Ivan Titov and Alexandre Klementiev. 2011. A Bayesian Model for Unsupervised Semantic Parsing. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1445–1455, Portland, OR, USA. Association for Computational Linguistics.
  • Titov and Klementiev (2012) Ivan Titov and Alexandre Klementiev. 2012. A Bayesian Approach to Unsupervised Semantic Role Induction. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 12–22, Avignon, France. Association for Computational Linguistics.
  • Tonelli and Pighin (2009) Sara Tonelli and Daniele Pighin. 2009. New Features for FrameNet - WordNet Mapping. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009), pages 219–227, Boulder, CO, USA. Association for Computational Linguistics.
  • Ustalov et al. (2017) Dmitry Ustalov, Alexander Panchenko, and Chris Biemann. 2017. Watset: Automatic Induction of Synsets from a Graph of Synonyms. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2017, pages 1579–1590, Vancouver, Canada. Association for Computational Linguistics.
  • Zhao and Zaki (2005) Lizhuang Zhao and Mohammed J. Zaki. 2005. TRICLUSTER: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data. In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, SIGMOD ’05, pages 694–705, New York, NY, USA. ACM.
Comments 0
Request Comment
You are adding the first comment!
How to quickly get a good reply:
  • Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made.
  • Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements.
  • Your comment should inspire ideas to flow and help the author improves the paper.

The better we are at sharing our knowledge with each other, the faster we move forward.
The feedback must be of minimum 40 characters and the title a minimum of 5 characters
Add comment
Loading ...
This is a comment super asjknd jkasnjk adsnkj
The feedback must be of minumum 40 characters
The feedback must be of minumum 40 characters

You are asking your first question!
How to quickly get a good answer:
  • Keep your question short and to the point
  • Check for grammar or spelling errors.
  • Phrase it like a question
Test description