112 research outputs found
Opinion Piece: Can we Fix the Scope for Coreference? Problems and Solutions for Benchmarks beyond OntoNotes
Current work on automatic coreference resolution has focused on the OntoNotes benchmark dataset, due to both its size and consistency. However many aspects of the OntoNotes annotation scheme are not well understood by NLP practitioners, including the treatment of generic NPs, noun modifiers, indefinite anaphora, predication and more. These often lead to counterintuitive claims, results and system behaviors. This opinion piece aims to highlight some of the problems with the OntoNotes rendition of coreference, and to propose a way forward relying on three principles: 1. a focus on semantics, not morphosyntax; 2. cross-linguistic generalizability; and 3. a separation of identity and scope, which can resolve old problems involving temporal and modal domain consistency
Report Linking: Information Extraction for Building Topical Knowledge Bases
Human language artifacts represent a plentiful source of rich, unstructured information created by reporters, scientists, and analysts. In this thesis we provide approaches for adding structure: extracting and linking entities, events, and relationships from a collection of documents about a common topic. We pursue this linking at two levels of abstraction. At the document level we propose models for aligning the entities and events described in coherent and related discourses: these models are useful for deduplicating repeated claims, finding implicit arguments to events, and measuring semantic overlap between documents. Then at a higher level of abstraction, we construct knowledge graphs containing salient entities and relations linked to supporting documents: these graphs can be augmented with facts and summaries to give users a structured understanding of the information in a large collection
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Advances in statistical script learning
When humans encode information into natural language, they do so with the
clear assumption that the reader will be able to seamlessly make inferences
based on world knowledge. For example, given the sentence ``Mrs. Dalloway said
she would buy the flowers herself,'' one can make a number of probable
inferences based on event co-occurrences: she bought flowers, she went to a
store, she took the flowers home, and so on.
Observing this, it is clear that many different useful natural language
end-tasks could benefit from models of events as they typically co-occur
(so-called script models).
Robust question-answering systems must be able to infer highly-probable implicit
events from what is explicitly stated in a text, as must robust
information-extraction systems that map from unstructured text to formal
assertions about relations expressed in the text. Coreference resolution
systems, semantic role labeling, and even syntactic parsing systems could, in
principle, benefit from event co-occurrence models.
To this end, we present a number of contributions related to statistical
event co-occurrence models. First, we investigate a method of incorporating
multiple entities into events in a count-based co-occurrence model. We find that
modeling multiple entities interacting across events allows for improved
empirical performance on the task of modeling sequences of events in documents.
Second, we give a method of applying Recurrent Neural Network sequence models
to the task of predicting held-out predicate-argument structures from documents.
This model allows us to easily incorporate entity noun information, and can
allow for more complex, higher-arity events than a count-based co-occurrence
model. We find the neural model improves performance considerably over the
count-based co-occurrence model.
Third, we investigate the performance of a sequence-to-sequence encoder-decoder
neural model on the task of predicting held-out predicate-argument events from
text. This model does not explicitly model any external syntactic information,
and does not require a parser. We find the text-level model to be competitive in
predictive performance with an event level model directly mediated by an
external syntactic analysis.
Finally, motivated by this result, we investigate incorporating features derived
from these models into a baseline noun coreference resolution system. We find
that, while our additional features do not appreciably improve top-level
performance, we can nonetheless provide empirical improvement on a number of
restricted classes of difficult coreference decisions.Computer Science
Aspects of Coherence for Entity Analysis
Natural language understanding is an important topic in natural language proces-
sing. Given a text, a computer program should, at the very least, be able to under-
stand what the text is about, and ideally also situate it in its extra-textual context
and understand what purpose it serves. What exactly it means to understand what a
text is about is an open question, but it is generally accepted that, at a minimum, un-
derstanding involves being able to answer questions like “Who did what to whom?
Where? When? How? And Why?”. Entity analysis, the computational analysis of
entities mentioned in a text, aims to support answering the questions “Who?” and
“Whom?” by identifying entities mentioned in a text. If the answers to “Where?”
and “When?” are specific, named locations and events, entity analysis can also pro-
vide these answers. Entity analysis aims to answer these questions by performing
entity linking, that is, linking mentions of entities to their corresponding entry in
a knowledge base, coreference resolution, that is, identifying all mentions in a text
that refer to the same entity, and entity typing, that is, assigning a label such as
Person to mentions of entities.
In this thesis, we study how different aspects of coherence can be exploited to
improve entity analysis. Our main contribution is a method that allows exploiting
knowledge-rich, specific aspects of coherence, namely geographic, temporal, and
entity type coherence. Geographic coherence expresses the intuition that entities
mentioned in a text tend to be geographically close. Similarly, temporal coherence
captures the intuition that entities mentioned in a text tend to be close in the tem-
poral dimension. Entity type coherence is based in the observation that in a text
about a certain topic, such as sports, the entities mentioned in it tend to have the
same or related entity types, such as sports team or athlete. We show how to integrate
features modeling these aspects of coherence into entity linking systems and esta-
blish their utility in extensive experiments covering different datasets and systems.
Since entity linking often requires computationally expensive joint, global optimi-
zation, we propose a simple, but effective rule-based approach that enjoys some of
the benefits of joint, global approaches, while avoiding some of their drawbacks.
To enable convenient error analysis for system developers, we introduce a tool for
visual analysis of entity linking system output. Investigating another aspect of co-
herence, namely the coherence between a predicate and its arguments, we devise a
distributed model of selectional preferences and assess its impact on a neural core-
ference resolution system. Our final contribution examines how multilingual entity
typing can be improved by incorporating subword information. We train and make
publicly available subword embeddings in 275 languages and show their utility in
a multilingual entity typing tas
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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
Inducing Implicit Arguments via Cross-document Alignment: A Framework and its Applications
Natural language texts frequently contain related information in different positions in discourse. As human readers, we can recognize such information across sentence boundaries and correctly infer relations between them. Given this inference capability, we understand texts that describe complex dependencies even if central aspects are not repeated in every sentence. In linguistics, certain omissions of redundant information are known under the term ellipsis and have been studied as cohesive devices in discourse (Halliday and Hasan, 1976). For computational approaches to semantic processing, such cohesive devices are problematic because methods are traditionally applied on the sentence level and barely take surrounding context
into account.
In this dissertation, we investigate omission phenomena on the level of predicate-argument structures. In particular, we examine instances of structures involving arguments that are not locally realized but inferable from context. The goal of this work is to automatically acquire and process such instances, which we also refer to as implicit arguments, to improve natural language processing applications. Our main contribution is a framework that identifies implicit arguments by aligning and comparing predicate-argument structures across pairs of comparable texts. As part of this framework, we develop a novel graph-based clustering approach, which detects corresponding predicate-argument structures using pairwise similarity metrics. To find discourse antecedents of implicit arguments, we further design a heuristic method that utilizes automatic annotations from various linguistic pre-processing tools.
We empirically validate the utility of automatically induced instances of implicit arguments and discourse antecedents in three extrinsic evaluation scenarios. In the first scenario, we show that our induced pairs of arguments and antecedents can successfully be applied to improve a pre-existing model for linking implicit arguments in discourse. In two further evaluation settings, we show that induced instances of implicit arguments, together with their aligned explicit counterparts, can be used as training material for a novel model of local coherence. Given discourse-level and semantic features, this model can predict whether a specific argument should be explicitly realized to establish local coherence or whether it is inferable and hence redundant in context
Coreference Resolution for Arabic
Recently, there has been enormous progress in coreference resolution. These recent developments were applied to Chinese, English and other languages, with outstanding results. However, languages with a rich morphology or fewer resources, such as Arabic, have not received as much attention. In fact, when this PhD work started there was no neural coreference resolver for Arabic, and we were not aware of any learning-based coreference resolver for Arabic since [Björkelund and Kuhn, 2014]. In addition, as far as we know, whereas lots of attention had been devoted to the phemomenon of zero anaphora in languages such as Chinese or Japanese, no neural model for Arabic zero-pronoun anaphora had been developed. In this thesis, we report on a series of experiments on Arabic coreference resolution in general and on zero anaphora in particular. We propose a new neural coreference resolver for Arabic, and we present a series of models for identifying and resolving Arabic zero pronouns. Our approach for zero-pronoun identification and resolution is applicable to other languages, and was also evaluated on Chinese, with results surpassing the state of the art at the time. This research also involved producing revised versions of standard datasets for Arabic coreference
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