Joint Inference

Recent work on Semantic Role Labeling (SRL) has shown that to achieve high accuracy a joint inference on the whole predicate argument structure should be ap- plied. In this paper, …...

In morphologically rich languages, should morphological and syntactic disambiguation be treated sequentially or as a sin- gle problem? We describe several efficient, probabilistically- interpretable ways to apply joint inference to …...

Machine learning approaches to coreference resolution are typically supervised, and re- quire expensive labeled data. Some unsuper- vised approaches have been proposed (e.g., Haghighi and Klein (2007)), but they are …...

This paper demonstrates a new method for leveraging free-text annotations to infer se- mantic properties of documents. Free-text an- notations are becoming increasingly abundant, due to the recent dramatic growth …...

We show that jointly performing semantic role labeling (SRL) on bitext can improve SRL results on both sides. In our approach, we use monolingual SRL systems to produce ar- gument …...

Many NLP tasks interact with syntax. The presence of a named entity span, for example, is often a clear indicator of a noun phrase in the parse tree, while a …...

This paper addresses the task of construct- ing a timeline of events mentioned in a given text. To accomplish that, we present a novel representation of the temporal structure of …...

Humans identify word boundaries in continu- ous speech by combining multiple cues; exist- ing state-of-the-art models, though, look at a single cue. We extend the generative model of Goldwater et …...

Coreference resolution systems can benefit greatly from inclusion of global context, and a number of recent approaches have demonstrated improvements when precom- puting an alignment to external knowledge sources. However, …...

We propose a joint inference algorithm for grammatical error correction. Different from most previous work where different error types are corrected independently, our proposed inference process considers all possible errors …...

State-of-the-art systems for grammatical er- ror correction are based on a collection of independently-trained models for specific er- rors. Such models ignore linguistic interac- tions at the sentence level and …...

Biological processes are complex phenom- ena involving a series of events that are re- lated to one another through various relation- ships. Systems that can understand and rea- son over …...

We address the problem of informal word recognition in Chinese microblogs. A key problem is the lack of word delimiters in Chinese. We exploit this reliance as an opportunity: recognizing …...

Sentence compression has been shown to benefit from joint inference involving both n-gram and dependency-factored objec- tives but this typically requires expensive integer programming. We explore instead the use of …...

AbstractDistant supervision is a successful paradigm that gathers training data for information extraction systems by automatically aligning vast databases of facts with text. Previous work has demonstrated its usefulness for …...

AbstractRecent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding …...

Models that learn to represent textual and knowledge base relations in the same con- tinuous latent space are able to perform joint inferences among the two kinds of re- lations …...

We submitted two systems to the SemEval- 2016 Task 12: Clinical TempEval challenge, participating in Phase 1, where we identified text spans of time and event expressions in clinical notes …...

Understanding the argumentative structure of a persuasive essay involves addressing two challenging tasks: identifying the components of the essay’s argument and identifying the relations that occur between them. We ex- …...

Semantic Role Labeling (SRL) captures se- mantic roles (or participants) such as agent, patient, and theme associated with verbs from the text. While it provides important in- termediate semantic representations …...

Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. …...

AbstractEvent coreference resolution is a challenging problem since it relies on several components of the information extraction pipeline that typically yield noisy outputs. We hypothesize that exploiting the inter-dependencies between …...

AbstractJoint inference approaches such as Integer Linear Programming (ILP) and Markov Logic Networks (MLNs) have recently been successfully applied to many natural language processing (NLP) tasks, often outperforming their pipeline …...

In timeline extraction the goal is to order all the events in which a target entity is involved in a timeline. Due to the lack of explic- itly annotated data, …...

AbstractLearning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, …...

AbstractThere has been considerable attention devoted to models that learn to jointly infer an expression’s syntactic structure and its semantics. Yet, Nangia and Bowman (2018) has recently shown that the …...

. This survey takes place in the general context of information extraction and presents more particularly the successive approaches to supervised event extraction from texts. The first rule-based systems and …...

. This survey takes place in the general context of information extraction and presents more particularly the successive approaches to supervised event extraction from texts. The first rule-based systems and …...

AbstractWe propose a graph-based event extraction framework JSEEGraph that approaches the task of event extraction as general graph parsing in the tradition of Meaning Representation Parsing. It explicitly encodes entities …...