1,720,973 research outputs found
Relation Extraction using Few-Shot Entailment on Conversational Data
No full textRelation Extraction is an important task for personal Natural Language Understanding,
and especially so in conversational applications where Knowledge Graphs, built from
such relations, are essential for knowledge storage. Recent advances in Natural Language
Understanding have shown that pre-trained Language Models tend to be the best at
solving various Language Understanding tasks. Most of the time, they are fine-tuned on
the specific downstream task, which relies on a large enough, high-quality dataset for the
task. To overcome this issue and improve model efficiency, the novel approach of Few-Shot
Learning is explored. Combined with the task of Natural Language Entailment, recent
research has shown that models using Few-Shot Learning and Inference can outperform
fully fine-tuned State-of-the-Art models on Relation Extraction tasks (and others). Since
relations are so important for conversational applications, the question is in how far the
approach of performing Relation Extraction with Entailment-based Few-Shot Learning
can be applied to conversational domains. Therefore, this thesis investigates in how far
this is applicable. The obtained results of the Few-Shot Entailment models tested do
not reach state-of-the-art approaches on relatively comparable tasks. Still, one main
conclusion is that entity type information is potentially an important factor for accurate
relation extraction, which is recommended for further research
Semantic Search with Knowledge Bases
No full textOver the past decade, modern search engines have made significant progress towards better understanding searchers' intents and providing them with more focused answers, a paradigm that is called \semantic search." Semantic search is a broad area that encompasses a variety of tasks and has a core enabling data component, called the knowledge base. In this thesis, we utilize knowledge bases to address three tasks involved in semantic search: (i) query understanding, (ii) entity retrieval, and (iii) entity summarization.
Query understanding is the first step in virtually every semantic search system. We study the problem of identifying entity mentions in queries and linking them to the corresponding entries in a knowledge base. We formulate this as the task of entity linking in queries, propose refinements to evaluation measures, and publish a test collection for training and evaluation purposes. We further establish a baseline method for this task through a reproducibility study, and introduce different methods with the aim to strike a balance between efficiency and effectiveness.
Next, we turn to using the obtained annotations for answering the queries. Here, our focus is on the entity retrieval task: answering search queries by returning a ranked list of entities. We introduce a general feature-based model based on Markov Random Fields, and show improvements over existing baseline methods. We find that the largest gains are achieved for complex natural language queries.
Having generated an answer to the query (from the entity retrieval step), we move on to presentation aspects of the results. We introduce and address the novel problem of dynamic entity summarization for entity cards, by breaking it into two subtasks, fact ranking and summary generation. We perform an extensive evaluation of our method using crowdsourcing, and show that our supervised fact ranking method brings substantial improvements over the most comparable baselines.
In this thesis, we take the reproducibility of our research very seriously. Therefore, all resources developed within the course of this work are made publicly available. We further make two major software and resource contributions: (i) the Nordlys toolkit, which implements a range of methods for semantic search, and (ii) the extended DBpedia-Entity test collection
An Adaptation Technique for GF-Based Dialogue Systems
No full textThis paper introduces a technique for adapting GF (Grammatical Framework)-based dialogue systems. This technique can be used to adapt dialogue systems in two aspects: user adaptation and self-adaptation. By user adaptation, users can customize the system to their own needs and define alternatives for a series of information to use in later utterances. By self-adaptation, the system can update GF grammar to keep the system adequate when new situations occur. This technique is demonstrated by a multi-lingual transport query system, which allows users to find up-to-date travel plans. Adapting GF-based dialogue systems improves the functionality of speech recognizers by defining alternatives for specific phrases and also keeps the dialogue system always updated
Semantic Search with Knowledge Bases
Get PDFOver the past decade, modern search engines have made significant progress towards better understanding searchers' intents and providing them with more focused answers, a paradigm that is called \semantic search." Semantic search is a broad area that encompasses a variety of tasks and has a core enabling data component, called the knowledge base. In this thesis, we utilize knowledge bases to address three tasks involved in semantic search: (i) query understanding, (ii) entity retrieval, and (iii) entity summarization.
Query understanding is the first step in virtually every semantic search system. We study the problem of identifying entity mentions in queries and linking them to the corresponding entries in a knowledge base. We formulate this as the task of entity linking in queries, propose refinements to evaluation measures, and publish a test collection for training and evaluation purposes. We further establish a baseline method for this task through a reproducibility study, and introduce different methods with the aim to strike a balance between efficiency and effectiveness.
Next, we turn to using the obtained annotations for answering the queries. Here, our focus is on the entity retrieval task: answering search queries by returning a ranked list of entities. We introduce a general feature-based model based on Markov Random Fields, and show improvements over existing baseline methods. We find that the largest gains are achieved for complex natural language queries.
Having generated an answer to the query (from the entity retrieval step), we move on to presentation aspects of the results. We introduce and address the novel problem of dynamic entity summarization for entity cards, by breaking it into two subtasks, fact ranking and summary generation. We perform an extensive evaluation of our method using crowdsourcing, and show that our supervised fact ranking method brings substantial improvements over the most comparable baselines.
In this thesis, we take the reproducibility of our research very seriously. Therefore, all resources developed within the course of this work are made publicly available. We further make two major software and resource contributions: (i) the Nordlys toolkit, which implements a range of methods for semantic search, and (ii) the extended DBpedia-Entity test collection
Adaptable Controlled Natural Languages for Online Query Systems
No full textThis workintroduces atechnique for adapting GF7 basedquery systems.This
technique is implemented in a travel planning query system in two ways.
Firstly, users can customize the systemto their own needs and definesynonyms for a series of information to use in later utterances. Secondly, the system can update GF grammar to make the system flexible when new situations occur. While implementing GF grammar adaptation in a query system, we introduce a design pattern formultilingual travel planning systems that allow users to find up to date%travel plans.The resulting system ca nbe easily ported to a new public transport network and communicates with a
transport web service to find accurate travelplans.Adapting GF7based dialogue systems improves the functionality of speech recognizers by defining
synonyms for specific phrases
Personal Entity, Concept, and Named Entity Linking in Conversations
Get PDFBuilding conversational agents that can have natural and knowledge-grounded
interactions with humans requires understanding user utterances. Entity Linking
(EL) is an effective and widely used method for understanding natural language
text and connecting it to external knowledge. It is, however, shown that
existing EL methods developed for annotating documents are suboptimal for
conversations, where personal entities (e.g., "my cars") and concepts are
essential for understanding user utterances. In this paper, we introduce a
collection and a tool for entity linking in conversations. We collect EL
annotations for 1327 conversational utterances, consisting of links to named
entities, concepts, and personal entities. The dataset is used for training our
toolkit for conversational entity linking, CREL. Unlike existing EL methods,
CREL is developed to identify both named entities and concepts. It also
utilizes coreference resolution techniques to identify personal entities and
references to the explicit entity mentions in the conversations. We compare
CREL with state-of-the-art techniques and show that it outperforms all existing
baselines
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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