Matching Items (16)
Filtering by
- All Subjects: Natural Language Processing
- Creators: Yang, Yezhou
- Creators: Anwar, Saadat
Description
The current study examines the social structure of local street gangs in Glendale, Arizona. Literature on gang organization has come to different conclusions about gang organization, largely based on the methodology used. One consistent finding from qualitative gang research has been that understanding the social connections between gang members is important for understanding how gangs are organized. The current study examines gang social structure by recreating gang social networks using official police data. Data on documented gang members, arrest records, and field interview cards from a 5-year period from 2006 to 2010 were used. Yearly social networks were constructed going two steps out from documented gang members. The findings indicated that gang networks had high turnover and they consisted of small subgroups. Further, the position of the gang member or associate was a significant predictor of arrest, specifically for those who had high betweenness centrality. At the group level, density and measures of centralization were not predictive of group-level behavior; hybrid groups were more likely to be involved in criminal behavior, however. The implications of these findings for both theory and policy are discussed.
ContributorsFox, Andrew (Author) / Katz, Charles M. (Thesis advisor) / White, Michael D. (Committee member) / Sweeten, Gary (Committee member) / Arizona State University (Publisher)
Created2013
Description
Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.
ContributorsRawal, Samarth Chetan (Author) / Baral, Chitta (Thesis director) / Anwar, Saadat (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
It is not merely an aggregation of static entities that a video clip carries, but alsoa variety of interactions and relations among these entities. Challenges still remain
for a video captioning system to generate natural language descriptions focusing on
the prominent interest and aligning with the latent aspects beyond observations. This
work presents a Commonsense knowledge Anchored Video cAptioNing (dubbed as
CAVAN) approach. CAVAN exploits inferential commonsense knowledge to assist the
training of video captioning model with a novel paradigm for sentence-level semantic
alignment. Specifically, commonsense knowledge is queried to complement per training
caption by querying a generic knowledge atlas ATOMIC, and form the commonsense-
caption entailment corpus. A BERT based language entailment model trained from
this corpus then serves as a commonsense discriminator for the training of video
captioning model, and penalizes the model from generating semantically misaligned
captions. With extensive empirical evaluations on MSR-VTT, V2C and VATEX
datasets, CAVAN consistently improves the quality of generations and shows higher
keyword hit rate. Experimental results with ablations validate the effectiveness of
CAVAN and reveals that the use of commonsense knowledge contributes to the video
caption generation.
ContributorsShao, Huiliang (Author) / Yang, Yezhou (Thesis advisor) / Jayasuriya, Suren (Committee member) / Xiao, Chaowei (Committee member) / Arizona State University (Publisher)
Created2022
Description
How to teach a machine to understand natural language? This question is a long-standing challenge in Artificial Intelligence. Several tasks are designed to measure the progress of this challenge. Question Answering is one such task that evaluates a machine's ability to understand natural language, where it reads a passage of text or an image and answers comprehension questions. In recent years, the development of transformer-based language models and large-scale human-annotated datasets has led to remarkable progress in the field of question answering. However, several disadvantages of fully supervised question answering systems have been observed. Such as generalizing to unseen out-of-distribution domains, linguistic style differences in questions, and adversarial samples. This thesis proposes implicitly supervised question answering systems trained using knowledge acquisition from external knowledge sources and new learning methods that provide inductive biases to learn question answering. In particular, the following research projects are discussed: (1) Knowledge Acquisition methods: these include semantic and abductive information retrieval for seeking missing knowledge, a method to represent unstructured text corpora as a knowledge graph, and constructing a knowledge base for implicit commonsense reasoning. (2) Learning methods: these include Knowledge Triplet Learning, a method over knowledge graphs; Test-Time Learning, a method to generalize to an unseen out-of-distribution context; WeaQA, a method to learn visual question answering using image captions without strong supervision; WeaSel, weakly supervised method for relative spatial reasoning; and a new paradigm for unsupervised natural language inference. These methods potentially provide a new research direction to overcome the pitfalls of direct supervision.
ContributorsBanerjee, Pratyay (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Committee member) / Blanco, Eduardo (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Enabling robots to physically engage with their environment in a safe and efficient manner is an essential step towards human-robot interaction. To date, robots usually operate as pre-programmed workers that blindly execute tasks in highly structured environments crafted by skilled engineers. Changing the robots’ behavior to cover new duties or handle variability is an expensive, complex, and time-consuming process. However, with the advent of more complex sensors and algorithms, overcoming these limitations becomes within reach. This work proposes innovations in artificial intelligence, language understanding, and multimodal integration to enable next-generation grasping and manipulation capabilities in autonomous robots. The underlying thesis is that multimodal observations and instructions can drastically expand the responsiveness and dexterity of robot manipulators. Natural language, in particular, can be used to enable intuitive, bidirectional communication between a human user and the machine. To this end, this work presents a system that learns context-aware robot control policies from multimodal human demonstrations. Among the main contributions presented are techniques for (a) collecting demonstrations in an efficient and intuitive fashion, (b) methods for leveraging physical contact with the environment and objects, (c) the incorporation of natural language to understand context, and (d) the generation of robust robot control policies. The presented approach and systems are evaluated in multiple grasping and manipulation settings ranging from dexterous manipulation to pick-and-place, as well as contact-rich bimanual insertion tasks. Moreover, the usability of these innovations, especially when utilizing human task demonstrations and communication interfaces, is evaluated in several human-subject studies.
ContributorsStepputtis, Simon (Author) / Ben Amor, Heni (Thesis advisor) / Baral, Chitta (Committee member) / Yang, Yezhou (Committee member) / Lee, Stefan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Models that learn from data are widely and rapidly being deployed today for real-world use, and have become an integral and embedded part of human lives. While these technological advances are exciting and impactful, such data-driven computer vision systems often fail in inscrutable ways. This dissertation seeks to study and improve the reliability of machine learning models from several perspectives including the development of robust training algorithms to mitigate the risks of such failures, construction of new datasets that provide a new perspective on capabilities of vision models, and the design of evaluation metrics for re-calibrating the perception of performance improvements. I will first address distribution shift in image classification with the following contributions: (1) two methods for improving the robustness of image classifiers to distribution shift by leveraging the classifier's failures into an adversarial data transformation pipeline guided by domain knowledge, (2) an interpolation-based technique for flagging out-of-distribution samples, and (3) an intriguing trade-off between distributional and adversarial robustness resulting from data modification strategies. I will then explore reliability considerations for \textit{semantic vision} models that learn from both visual and natural language data; I will discuss how logical and semantic sentence transformations affect the performance of vision--language models and my contributions towards developing knowledge-guided learning algorithms to mitigate these failures. Finally, I will describe the effort towards building and evaluating complex reasoning capabilities of vision--language models towards the long-term goal of robust and reliable computer vision models that can communicate, collaborate, and reason with humans.
ContributorsGokhale, Tejas (Author) / Yang, Yezhou (Thesis advisor) / Baral, Chitta (Thesis advisor) / Ben Amor, Heni (Committee member) / Anirudh, Rushil (Committee member) / Arizona State University (Publisher)
Created2023
Description
In the era of information explosion and multi-modal data, information retrieval (IR) and question answering (QA) systems have become essential in daily human activities. IR systems aim to find relevant information in response to user queries, while QA systems provide concise and accurate answers to user questions. IR and QA are two of the most crucial challenges in the realm of Artificial Intelligence (AI), with wide-ranging real-world applications such as search engines and dialogue systems.
This dissertation investigates and develops novel models and training objectives to enhance current retrieval systems in textual and multi-modal contexts. Moreover, it examines QA systems, emphasizing generalization and robustness, and creates new benchmarks to promote their progress. Neural retrievers have surfaced as a viable solution, capable of surpassing the constraints of traditional term-matching search algorithms. This dissertation presents Poly-DPR, an innovative multi-vector model architecture that manages test-query, and ReViz, a comprehensive multimodal model to tackle multi-modality queries. By utilizing IR-focused pretraining tasks and producing large-scale training data, the proposed methodology substantially improves the abilities of existing neural retrievers.Concurrently, this dissertation investigates the realm of QA systems, referred to as ``readers'', by performing an exhaustive analysis of current extractive and generative readers, which results in a reliable guidance for selecting readers for downstream applications. Additionally, an original reader (Two-in-One) is designed to effectively choose the pertinent passages and sentences from a pool of candidates for multi-hop reasoning. This dissertation also acknowledges the significance of logical reasoning in real-world applications and has developed a comprehensive testbed, LogiGLUE, to further the advancement of reasoning capabilities in QA systems.
ContributorsLuo, Man (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Committee member) / Blanco, Eduardo (Committee member) / Chen, Danqi (Committee member) / Arizona State University (Publisher)
Created2023
Description
Reasoning with commonsense knowledge is an integral component of human behavior. It is due to this capability that people know that a weak person may not be able to lift someone. It has been a long standing goal of the Artificial Intelligence community to simulate such commonsense reasoning abilities in machines. Over the years, many advances have been made and various challenges have been proposed to test their abilities. The Winograd Schema Challenge (WSC) is one such Natural Language Understanding (NLU) task which was also proposed as an alternative to the Turing Test. It is made up of textual question answering problems which require resolution of a pronoun to its correct antecedent.
In this thesis, two approaches of developing NLU systems to solve the Winograd Schema Challenge are demonstrated. To this end, a semantic parser is presented, various kinds of commonsense knowledge are identified, techniques to extract commonsense knowledge are developed and two commonsense reasoning algorithms are presented. The usefulness of the developed tools and techniques is shown by applying them to solve the challenge.
In this thesis, two approaches of developing NLU systems to solve the Winograd Schema Challenge are demonstrated. To this end, a semantic parser is presented, various kinds of commonsense knowledge are identified, techniques to extract commonsense knowledge are developed and two commonsense reasoning algorithms are presented. The usefulness of the developed tools and techniques is shown by applying them to solve the challenge.
ContributorsSharma, Arpita (Author) / Baral, Chitta (Thesis advisor) / Lee, Joohyung (Committee member) / Papotti, Paolo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019
Description
In recent years, several methods have been proposed to encode sentences into fixed length continuous vectors called sentence representation or sentence embedding. With the recent advancements in various deep learning methods applied in Natural Language Processing (NLP), these representations play a crucial role in tasks such as named entity recognition, question answering and sentence classification.
Traditionally, sentence vector representations are learnt from its constituent word representations, also known as word embeddings. Various methods to learn the distributed representation (embedding) of words have been proposed using the notion of Distributional Semantics, i.e. “meaning of a word is characterized by the company it keeps”. However, principle of compositionality states that meaning of a sentence is a function of the meanings of words and also the way they are syntactically combined. In various recent methods for sentence representation, the syntactic information like dependency or relation between words have been largely ignored.
In this work, I have explored the effectiveness of sentence representations that are composed of the representation of both, its constituent words and the relations between the words in a sentence. The word and relation embeddings are learned based on their context. These general-purpose embeddings can also be used as off-the- shelf semantic and syntactic features for various NLP tasks. Similarity Evaluation tasks was performed on two datasets showing the usefulness of the learned word embeddings. Experiments were conducted on three different sentence classification tasks showing that our sentence representations outperform the original word-based sentence representations, when used with the state-of-the-art Neural Network architectures.
Traditionally, sentence vector representations are learnt from its constituent word representations, also known as word embeddings. Various methods to learn the distributed representation (embedding) of words have been proposed using the notion of Distributional Semantics, i.e. “meaning of a word is characterized by the company it keeps”. However, principle of compositionality states that meaning of a sentence is a function of the meanings of words and also the way they are syntactically combined. In various recent methods for sentence representation, the syntactic information like dependency or relation between words have been largely ignored.
In this work, I have explored the effectiveness of sentence representations that are composed of the representation of both, its constituent words and the relations between the words in a sentence. The word and relation embeddings are learned based on their context. These general-purpose embeddings can also be used as off-the- shelf semantic and syntactic features for various NLP tasks. Similarity Evaluation tasks was performed on two datasets showing the usefulness of the learned word embeddings. Experiments were conducted on three different sentence classification tasks showing that our sentence representations outperform the original word-based sentence representations, when used with the state-of-the-art Neural Network architectures.
ContributorsRath, Trideep (Author) / Baral, Chitta (Thesis advisor) / Li, Baoxin (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
Description
Information Retrieval (IR) is the task of obtaining pieces of data (such as documents or snippets of text) that are relevant to a particular query or need from a large repository of information. IR is a valuable component of several downstream Natural Language Processing (NLP) tasks, such as Question Answering. Practically, IR is at the heart of many widely-used technologies like search engines.
While probabilistic ranking functions, such as the Okapi BM25 function, have been utilized in IR systems since the 1970's, modern neural approaches pose certain advantages compared to their classical counterparts. In particular, the release of BERT (Bidirectional Encoder Representations from Transformers) has had a significant impact in the NLP community by demonstrating how the use of a Masked Language Model (MLM) trained on a considerable corpus of data can improve a variety of downstream NLP tasks, including sentence classification and passage re-ranking.
IR Systems are also important in the biomedical and clinical domains. Given the continuously-increasing amount of scientific literature across biomedical domain, the ability find answers to specific clinical queries from a repository of millions of articles is a matter of practical value to medics, doctors, and other medical professionals. Moreover, there are domain-specific challenges present in the biomedical domain, including handling clinical jargon and evaluating the similarity or relatedness of various medical symptoms when determining the relevance between a query and a sentence.
This work presents contributions to several aspects of the Biomedical Semantic Information Retrieval domain. First, it introduces Multi-Perspective Sentence Relevance, a novel methodology of utilizing BERT-based models for contextual IR. The system is evaluated using the BioASQ Biomedical IR Challenge. Finally, practical contributions in the form of a live IR system for medics and a proposed challenge on the Living Systematic Review clinical task are provided.
While probabilistic ranking functions, such as the Okapi BM25 function, have been utilized in IR systems since the 1970's, modern neural approaches pose certain advantages compared to their classical counterparts. In particular, the release of BERT (Bidirectional Encoder Representations from Transformers) has had a significant impact in the NLP community by demonstrating how the use of a Masked Language Model (MLM) trained on a considerable corpus of data can improve a variety of downstream NLP tasks, including sentence classification and passage re-ranking.
IR Systems are also important in the biomedical and clinical domains. Given the continuously-increasing amount of scientific literature across biomedical domain, the ability find answers to specific clinical queries from a repository of millions of articles is a matter of practical value to medics, doctors, and other medical professionals. Moreover, there are domain-specific challenges present in the biomedical domain, including handling clinical jargon and evaluating the similarity or relatedness of various medical symptoms when determining the relevance between a query and a sentence.
This work presents contributions to several aspects of the Biomedical Semantic Information Retrieval domain. First, it introduces Multi-Perspective Sentence Relevance, a novel methodology of utilizing BERT-based models for contextual IR. The system is evaluated using the BioASQ Biomedical IR Challenge. Finally, practical contributions in the form of a live IR system for medics and a proposed challenge on the Living Systematic Review clinical task are provided.
ContributorsRawal, Samarth (Author) / Baral, Chitta (Thesis advisor) / Devarakonda, Murthy (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)
Created2020