Representation Learning for Words and Entities

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.Comment: phd thesis, Machine Learning, Natural Language Processing, Representation Learning, Knowledge Graphs, Entities, Word Embeddings, Entity Embedding

Representation Learning for Words and Entities

This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview LSA (MVLSA). Through experiments on close to 50 different views, I show that MVLSA outperforms other state-of-the-art word embedding models. After that, I focus on learning entity representations for search and recommendation and present the second algorithm of this thesis called Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. Moreover, I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints

Web knowledge bases

Knowledge is key to natural language understanding. References to specific people, places and things in text are crucial to resolving ambiguity and extracting meaning. Knowledge Bases (KBs) codify this information for automated systems — enabling applications such as entity-based search and question answering. This thesis explores the idea that sites on the web may act as a KB, even if that is not their primary intent. Dedicated kbs like Wikipedia are a rich source of entity information, but are built and maintained at an ongoing cost in human effort. As a result, they are generally limited in terms of the breadth and depth of knowledge they index about entities. Web knowledge bases offer a distributed solution to the problem of aggregating entity knowledge. Social networks aggregate content about people, news sites describe events with tags for organizations and locations, and a diverse assortment of web directories aggregate statistics and summaries for long-tail entities notable within niche movie, musical and sporting domains. We aim to develop the potential of these resources for both web-centric entity Information Extraction (IE) and structured KB population. We first investigate the problem of Named Entity Linking (NEL), where systems must resolve ambiguous mentions of entities in text to their corresponding node in a structured KB. We demonstrate that entity disambiguation models derived from inbound web links to Wikipedia are able to complement and in some cases completely replace the role of resources typically derived from the KB. Building on this work, we observe that any page on the web which reliably disambiguates inbound web links may act as an aggregation point for entity knowledge. To uncover these resources, we formalize the task of Web Knowledge Base Discovery (KBD) and develop a system to automatically infer the existence of KB-like endpoints on the web. While extending our framework to multiple KBs increases the breadth of available entity knowledge, we must still consolidate references to the same entity across different web KBs. We investigate this task of Cross-KB Coreference Resolution (KB-Coref) and develop models for efficiently clustering coreferent endpoints across web-scale document collections. Finally, assessing the gap between unstructured web knowledge resources and those of a typical KB, we develop a neural machine translation approach which transforms entity knowledge between unstructured textual mentions and traditional KB structures. The web has great potential as a source of entity knowledge. In this thesis we aim to first discover, distill and finally transform this knowledge into forms which will ultimately be useful in downstream language understanding tasks

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

IMAGE RETRIEVAL BASED ON COMPLEX DESCRIPTIVE QUERIES

The amount of visual data such as images and videos available over web has increased exponentially over the last few years. In order to efficiently organize and exploit these massive collections, a system, apart from being able to answer simple classification based questions such as whether a specific object is present (or absent) in an image, should also be capable of searching images and videos based on more complex descriptive questions. There is also a considerable amount of structure present in the visual world which, if effectively utilized, can help achieve this goal. To this end, we first present an approach for image ranking and retrieval based on queries consisting of multiple semantic attributes. We further show that there are significant correlations present between these attributes and accounting for them can lead to superior performance. Next, we extend this by proposing an image retrieval framework for descriptive queries composed of object categories, semantic attributes and spatial relationships. The proposed framework also includes a unique multi-view hashing technique, which enables query specification in three different modalities - image, sketch and text. We also demonstrate the effectiveness of leveraging contextual information to reduce the supervision requirements for learning object and scene recognition models. We present an active learning framework to simultaneously learn appearance and contextual models for scene understanding. Within this framework we introduce new kinds of labeling questions that are designed to collect appearance as well as contextual information and which mimic the way in which humans actively learn about their environment. Furthermore we explicitly model the contextual interactions between the regions within an image and select the question which leads to the maximum reduction in the combined entropy of all the regions in the image (image entropy)

深層学習に基づく感情会話分析に関する研究

Owning the capability to express specific emotions by a chatbot during a conversation is one of the key parts of artificial intelligence, which has an intuitive and quantifiable impact on the improvement of chatbot’s usability and user satisfaction. Enabling machines to emotion recognition in conversation is challenging, mainly because the information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. Recently, many studies on neural emotional conversational models have been conducted. However, enabling the chatbot to control what kind of emotion to respond to upon its own characters in conversation is still underexplored. At this stage, people are no longer satisfied with using a dialogue system to solve specific tasks, and are more eager to achieve spiritual communication. In the chat process, if the robot can perceive the user's emotions and can accurately process them, it can greatly enrich the content of the dialogue and make the user empathize. In the process of emotional dialogue, our ultimate goal is to make the machine understand human emotions and give matching responses. Based on these two points, this thesis explores and in-depth emotion recognition in conversation task and emotional dialogue generation task. In the past few years, although considerable progress has been made in emotional research in dialogue, there are still some difficulties and challenges due to the complex nature of human emotions. The key contributions in this thesis are summarized as below: (1) Researchers have paid more attention to enhancing natural language models with knowledge graphs these days, since knowledge graph has gained a lot of systematic knowledge. A large number of studies had shown that the introduction of external commonsense knowledge is very helpful to improve the characteristic information. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. In this work, we employ an external knowledge graph ATOMIC to extract the knowledge sources. We proposed KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. The conversation is a sequence of coherent and orderly discourses. For neural networks, the capture of long-range context information is a weakness. We adopt Transformer a structure composed of self-attention and feed forward neural network, instead of the traditional RNN model, aiming at capturing remote context information. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets. (2) We proposed an emotional dialogue model based on Seq2Seq, which is improved from three aspects: model input, encoder structure, and decoder structure, so that the model can generate responses with rich emotions, diversity, and context. In terms of model input, emotional information and location information are added based on word vectors. In terms of the encoder, the proposed model first encodes the current input and sentence sentiment to generate a semantic vector, and additionally encodes the context and sentence sentiment to generate a context vector, adding contextual information while ensuring the independence of the current input. On the decoder side, attention is used to calculate the weights of the two semantic vectors separately and then decode, to fully integrate the local emotional semantic information and the global emotional semantic information. We used seven objective evaluation indicators to evaluate the model's generation results, context similarity, response diversity, and emotional response. Experimental results show that the model can generate diverse responses with rich sentiment, contextual associations

Interactive video retrieval

Video storage, analysis, and retrieval has become an important research topic recently due to the advancements in the creation and distribution of video data. In this thesis, an investigation into interactive video retrieval is presented. Advanced feedback techniques have been investigated in the retrieval of textual data. Novel interactive schemes, mainly based on the concept of relevance feedback, have been developed and experimented. However, such approaches have not been applied in the video retrieval domain. In this thesis, we investigate the use of advanced interactive retrieval schemes for the retrieval of video data. To understand the role of various features for the video retrieval, we experimented with various retrieval strategies. We benchmarked the role of visual features, the textual features and their combination. To explore this further, we categorized query into various classes and investigated the retrieval effectiveness of various features and their combination. Based on the results, we developed a retrieval scheme for video retrieval. We developed an interactive retrieval technique based on the concept of implicit feedback. A number of retrieval models are developed based on this concept and benchmarked with a simulation- based evaluation strategy. A Binary Voting Model performed well and has been reformed for user-based experiments. We experimented with the users and compared the performance of an interactive retrieval system, using a combination of implicit and explicit feedback techniques, with that of a system using explicit feedback techniques

Proceedings of the Conference on Natural Language Processing 2010

This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field