617 research outputs found
Semantic Parsing for Question Answering over Knowledge Graphs
In this paper, we introduce a novel method with graph-to-segment mapping for
question answering over knowledge graphs, which helps understanding question
utterances. This method centers on semantic parsing, a key approach for
interpreting these utterances. The challenges lie in comprehending implicit
entities, relationships, and complex constraints like time, ordinality, and
aggregation within questions, contextualized by the knowledge graph. Our
framework employs a combination of rule-based and neural-based techniques to
parse and construct highly accurate and comprehensive semantic segment
sequences. These sequences form semantic query graphs, effectively representing
question utterances. We approach question semantic parsing as a sequence
generation task, utilizing an encoder-decoder neural network to transform
natural language questions into semantic segments. Moreover, to enhance the
parsing of implicit entities and relations, we incorporate a graph neural
network that leverages the context of the knowledge graph to better understand
question representations. Our experimental evaluations on two datasets
demonstrate the effectiveness and superior performance of our model in semantic
parsing for question answering.Comment: arXiv admin note: text overlap with arXiv:2401.0296
Unsupervised Chunking with Hierarchical RNN
In Natural Language Processing (NLP), predicting linguistic structures, such
as parsing and chunking, has mostly relied on manual annotations of syntactic
structures. This paper introduces an unsupervised approach to chunking, a
syntactic task that involves grouping words in a non-hierarchical manner. We
present a two-layer Hierarchical Recurrent Neural Network (HRNN) designed to
model word-to-chunk and chunk-to-sentence compositions. Our approach involves a
two-stage training process: pretraining with an unsupervised parser and
finetuning on downstream NLP tasks. Experiments on the CoNLL-2000 dataset
reveal a notable improvement over existing unsupervised methods, enhancing
phrase F1 score by up to 6 percentage points. Further, finetuning with
downstream tasks results in an additional performance improvement.
Interestingly, we observe that the emergence of the chunking structure is
transient during the neural model's downstream-task training. This study
contributes to the advancement of unsupervised syntactic structure discovery
and opens avenues for further research in linguistic theory
Domain adaptation with minimal training
The performance of a machine learning model trained on labeled data of a (source) domain degrades severely when they are tested on a different (target) domain. Traditional approaches deal with this problem by training a new model for every target domain. In natural language processing, top performing systems often use multiple interconnected models; therefore training all of them for every target domain is computationally expensive. Moreover, retraining the model for the target domain requires access to the labeled data from the source domain which may not be available to end users due to copyright issues. This thesis is a study on how to adapt to a target domain, using the system trained on source domain and avoiding the cost of retraining and the need for access to the source labeled data.
This thesis identifies two key ingredients for adaptation without training: broad coverage resources and constraints.
We show how resources like Wikipedia, VerbNet and WordNet that contain comprehensive coverage of entities, semantic roles and words in English can help a model adapt to the target domain. For the task of semantic role labeling, we show that in the decision phase, we can replace a linguistic unit (e.g. verb, word) with another equivalent linguistic unit residing in the same cluster defined in these resources (e.g. VerbNet, WordNet) such that after replacement, text becomes more like text on which the model was trained. We show that the model's output is more accurate on the transformed text than on original text. In another instance, we show how to use a system for linking mentions to Wikipedia concepts for adaptation of a named entity recognition system. Since Wikipedia has a broad domain coverage, the linking system is robust across domain variations. Therefore, jointly performing entity recognition and linking improves the accuracy of entity recognition on the target domain without requiring training of a new system for the new domain. In all cases, we show how to use intuitive constraints to guide the model into making coherent predictions.
We show how incorporating prior knowledge about a new domain as declarative constraints into the decision phase can improve performance of a model on the new domain. When such prior knowledge is unavailable, we show how to acquire knowledge automatically from unlabeled text from the new domain and domains similar to both source and target domains
Multiword expression processing: A survey
Multiword expressions (MWEs) are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives
Natural Language Processing and Graph Representation Learning for Clinical Data
The past decade has witnessed remarkable progress in biomedical informatics and its related fields: the development of high-throughput technologies in genomics, the mass adoption of electronic health records systems, and the AI renaissance largely catalyzed by deep learning. Deep learning has played an undeniably important role in our attempts to reduce the gap between the exponentially growing amount of biomedical data and our ability to make sense of them. In particular, the two main pillars of this dissertation---natural language processing and graph representation learning---have improved our capacity to learn useful representations of language and structured data to an extent previously considered unattainable in such a short time frame. In the context of clinical data, characterized by its notorious heterogeneity and complexity, natural language processing and graph representation learning have begun to enrich our toolkits for making sense and making use of the wealth of biomedical data beyond rule-based systems or traditional regression techniques. This dissertation comes at the cusp of such a paradigm shift, detailing my journey across the fields of biomedical and clinical informatics through the lens of natural language processing and graph representation learning. The takeaway is quite optimistic: despite the many layers of inefficiencies and challenges in the healthcare ecosystem, AI for healthcare is gearing up to transform the world in new and exciting ways
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