Unsupervised semantic frame induction using triclustering

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

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.Comment: 8 pages, 1 figure, 4 tables, accepted at ACL 201

Strategies to Address Data Sparseness in Implicit Semantic Role Labeling

Natural language texts frequently contain predicates whose complete understanding re- quires access to other parts of the discourse. Human readers can retrieve such infor- mation across sentence boundaries and infer the implicit piece of information. This capability enables us to understand complicated texts without needing to repeat the same information in every single sentence. However, for computational systems, resolv- ing such information is problematic because computational approaches traditionally rely on sentence-level processing and rarely take into account the extra-sentential context. In this dissertation, we investigate this omission phenomena, called implicit semantic role labeling. Implicit semantic role labeling involves identification of predicate argu- ments that are not locally realized but are resolvable from the context. For example, in ”What’s the matter, Walters? asked Baynes sharply.”, the ADDRESSEE of the predicate ask, Walters, is not mentioned as one of its syntactic arguments, but can be recoverable from the previous sentence. In this thesis, we try to improve methods for the automatic processing of such predicate instances to improve natural language pro- cessing applications. Our main contribution is introducing approaches to solve the data sparseness problem of the task. We improve automatic identification of implicit roles by increasing the amount of training set without needing to annotate new instances. For this purpose, we propose two approaches. As the first one, we use crowdsourcing to annotate instances of implicit semantic roles and show that with an appropriate task de- sign, reliable annotation of implicit semantic roles can be obtained from the non-experts without the need to present precise and linguistic definition of the roles to them. As the second approach, we combine seemingly incompatible corpora to solve the problem of data sparseness of ISRL by applying a domain adaptation technique. We show that out of domain data from a different genre can be successfully used to improve a baseline implicit semantic role labeling model, when used with an appropriate domain adapta- tion technique. The results also show that the improvement occurs regardless of the predicate part of speech, that is, identification of implicit roles relies more on semantic features than syntactic ones. Therefore, annotating instances of nominal predicates, for instance, can help to improve identification of verbal predicates’ implicit roles, we well. Our findings also show that the variety of the additional data is more important than its size. That is, increasing a large amount of data does not necessarily lead to a better model

Causal schema induction for knowledge discovery

Making sense of familiar yet new situations typically involves making generalizations about causal schemas, stories that help humans reason about event sequences. Reasoning about events includes identifying cause and effect relations shared across event instances, a process we refer to as causal schema induction. Statistical schema induction systems may leverage structural knowledge encoded in discourse or the causal graphs associated with event meaning, however resources to study such causal structure are few in number and limited in size. In this work, we investigate how to apply schema induction models to the task of knowledge discovery for enhanced search of English-language news texts. To tackle the problem of data scarcity, we present Torquestra, a manually curated dataset of text-graph-schema units integrating temporal, event, and causal structures. We benchmark our dataset on three knowledge discovery tasks, building and evaluating models for each. Results show that systems that harness causal structure are effective at identifying texts sharing similar causal meaning components rather than relying on lexical cues alone. We make our dataset and models available for research purposes.Comment: 8 pages, appendi

Learning better latent representations from semantic knowledge

Many modern efforts in Natural Language Processing involve the use of deep neural network models, where dense vector representations are learned for words and sentences, and they have been proven effective in many downstream tasks. However, it remains unknown whether these representations truly understand the meaning of language, due to their vulnerability against adversarial attacks and lack of generalization ability to unseen domains. In this thesis, we investigate the use of semantic knowledge to help learn better representations from neural models. We start with a certain type of semantic phenomenon, the implicit predicate-argument relations, and propose two neural models that draw on narrative event coherence and entity salience. We also introduce an argument cloze task for the automatic creation of synthetic data at scale from structural representations of events and entities. We demonstrate that when trained with large-scale synthetic data, both these models show good performance on a human-annotated dataset for nominal implicit arguments. We then focus on the integration of a broader range of semantic knowledge into neural models in a more latent manner. We find that by injecting coreference knowledge as auxiliary supervision for self-attention, a relatively small model sets the state-of-the-art on a word prediction task specifically designed to require long-distance reasoning. We further explore different ways of integrating semantic knowledge into large-scale pre-trained language models to make them more generalizable at out-of-domain question answering tasks, and show some preliminary results.Computer Science

Unrestricted Bridging Resolution

Anaphora plays a major role in discourse comprehension and accounts for the coherence of a text. In contrast to identity anaphora which indicates that a noun phrase refers back to the same entity introduced by previous descriptions in the discourse, bridging anaphora or associative anaphora links anaphors and antecedents via lexico-semantic, frame or encyclopedic relations. In recent years, various computational approaches have been developed for bridging resolution. However, most of them only consider antecedent selection, assuming that bridging anaphora recognition has been performed. Moreover, they often focus on subproblems, e.g., only part-of bridging or definite noun phrase anaphora. This thesis addresses the problem of unrestricted bridging resolution, i.e., recognizing bridging anaphora and finding links to antecedents where bridging anaphors are not limited to definite noun phrases and semantic relations between anaphors and their antecedents are not restricted to meronymic relations. In this thesis, we solve the problem using a two-stage statistical model. Given all mentions in a document, the first stage predicts bridging anaphors by exploring a cascading collective classification model. We cast bridging anaphora recognition as a subtask of learning fine-grained information status (IS). Each mention in a text gets assigned one IS class, bridging being one possible class. The model combines the binary classifiers for minority categories and a collective classifier for all categories in a cascaded way. It addresses the multi-class imbalance problem (e.g., the wide variation of bridging anaphora and their relative rarity compared to many other IS classes) within a multi-class setting while still keeping the strength of the collective classifier by investigating relational autocorrelation among several IS classes. The second stage finds the antecedents for all predicted bridging anaphors at the same time by exploring a joint inference model. The approach models two mutually supportive tasks (i.e., bridging anaphora resolution and sibling anaphors clustering) jointly, on the basis of the observation that semantically/syntactically related anaphors are likely to be sibling anaphors, and hence share the same antecedent. Both components are based on rich linguistically-motivated features and discriminatively trained on a corpus (ISNotes) where bridging is reliably annotated. Our approaches achieve substantial improvements over the reimplementations of previous systems for all three tasks, i.e., bridging anaphora recognition, bridging anaphora resolution and full bridging resolution. The work is – to our knowledge – the first bridging resolution system that handles the unrestricted phenomenon in a realistic setting. The methods in this dissertation were originally presented in Markert et al. (2012) and Hou et al. (2013a; 2013b; 2014). The thesis gives a detailed exposition, carrying out a thorough corpus analysis of bridging and conducting a detailed comparison of our models to others in the literature, and also presents several extensions of the aforementioned papers