Knowledge Base

-- An approach to extract linguis- tic knowledge from real language behavior is described. This method depends on the extraction of word relations, patterns of which are obtained by structuring …...

Ambiguities related to intension and their consequent inference failures are a diverse group, both syntacti- cally and semantically. One particular kind of ambi- guity that has received little attention so …...

Creating a knowledge base has always been a bottleneck in the implementation of AI systems. This is also true for Natural I,anguage Understanding (NhU) systems, particularly [or data-driven ones. While …...

Although natural language technology has achieved a high degree of domain independence through separating domain-independent modules from domain-dependent knowledge bases, portability, as measured by effort to move from one application …...

This paper presents a computational model of how conversational participants collaborate in making re- ferring expressions. The model is based on the plan- ning paradigm. It employs plans for constructing …...

We present a new architecture for reversible NLP. Separate parsing and generation grammars are constructed from the underlying application's semantic model and knowledge base. By having two grammars we are …...

This paper is an outline of a statistical learn- ing algorithm for information extraction sys- tems. It is based on a lexicaUy intensive anal- ysis of a small number of …...

Information Extraction (IE) systems today are com- monly based on pattern matching. The patterns are regular expressions stored in a customizable knowl- edge base. Adapting an IE system to a …...

Here we present work on using spatial knowl- edge in conjunction with information extrac- tion (IE). Considerable volume of location data was imported in a knowledge base (KB) with entities …...

Paraphrase recognition is a critical step for nat- ural language interpretation. Accordingly, many NLP applications would benefit from high coverage knowledge bases of paraphrases. However, the scal- ability of state-of-the-art …...

. In this paper, we first discuss a problem of developing a knowledge base by using natural language documents: wrong informa- tion in natural language documents. It is almost inevitable …...

This paper presents a new fully auto- matic method for building highly dense and accurate knowledge bases from ex- isting semantic resources. Basically, the method uses a wide-coverage and accu- …...

We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, recursively induces lambda forms …...

Determining whether two terms in text have an ancestor relation (e.g. Toyota and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in …...

AbstractThis paper presents a novel automatic approach to partially integrate FrameNet and WordNet. In that way we expect to extend FrameNet coverage, to enrich WordNet with frame semantic information and …...

AbstractAfter several years of development, the vision of the Semantic Web is gradually becoming reality. Large data repositories have been created and offer semantic information in a machine-processable form for …...

AbstractNatural language understanding systems require a knowledge base provided with conceptual representations reflecting the structure of human beings’ cognitive system. Although surface semantics can be sufficient in some other systems, …...

Entity linking disambiguates a mention of an entity in text to a Knowledge Base (KB). Most previous studies disambiguate a mention of a name (e.g.“AZ”) based on the distribution knowledge …...

In this paper we introduce a method for searching appropriate articles from knowledge bases (e.g. Wikipedia) for a given query and its context. In our approach, this problem is transformed …...

We present a method for training a semantic parser using only a knowledge base and an un- labeled text corpus, without any individually annotated sentences. Our key observation is that …...

In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we …...

This paper demonstrates the need and im- pact of subcategorization information for SMT. We combine (i) features on source- side syntactic subcategorization and (ii) an external knowledge base with quantita- …...

We present an approach to training a joint syntactic and semantic parser that com- bines syntactic training information from CCGbank with semantic training informa- tion from a knowledge base via …...

. This paper presents a knowledge base containing triples involving pairs of verbs associated with seman- tic or discourse relations. The relations in these triples are marked by discourse connectors …...

This paper presents a knowledge base containing triples involving pairs of verbs associated with semantic or discourse relations. The relations in these triples are marked by discourse connectors between two …...

Cross-document co-reference resolution (CCR) computes equivalence classes over textual mentions denoting the same entity in a document corpus. Named-entity link- ing (NEL) disambiguates mentions onto entities present in a knowledge …...

This paper reports on our research to generate multilingual semantic lexical resources and develop multilingual semantic annotation software, which assigns each word in running text to a semantic category based …...

AbstractWe present DefIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce …...

We consider a novel setting for Named En- tity Recognition (NER) where we have ac- cess to document-specific knowledge base tags. These tags consist of a canonical name from a …...

We present a corpus of 2,380 natural language queries paired with machine readable formu- lae that can be executed against world wide geographic data of the OpenStreetMap (OSM) database. We …...

AbstractKnowledge base (KB) such as Freebase plays an important role for many natural language processing tasks. English knowledge base is obviously larger and of higher quality than low resource language …...

Search personalization that considers the social dimension of the web has attracted a significant volume of research in recent years. A user pro- file is usually needed to represent a …...

AbstractKnowledge embedding, which projects triples in a given knowledge base to d-dimensional vectors, has attracted considerable research efforts recently. Most existing approaches treat the given knowledge base as a set …...

AbstractRecent trend in question answering (QA) systems focuses on using structured knowledge bases (KBs) to find answers. While these systems are able to provide more precise answers than information retrieval …...

In recent years many knowledge bases (KBs) have been constructed, yet there is not yet a verb resource that maps to these growing KB resources. A resource that maps verbs …...

We present a semi-automated framework for constructing factoid question answering (QA) datasets, where an array of question character- istics are formalized, including structure com- plexity, function, commonness, answer cardi- nality, …...

We propose a new open question answer- ing framework for question answering over a knowledge base (KB). Our system uses both a curated KB, Freebase, and one that is ex- …...

AbstractBesides providing the relevant information, amusing users has been an important role of the web. Many web sites provide serendipitous (unexpected but relevant) information to draw user traffic. In this …...

Lexical taxonomies are graph-like hierarchi- cal structures that provide a formal representa- tion of knowledge. Most knowledge graphs to date rely on is-a (hypernymic) relations as the backbone of their …...

Question answering requires access to a knowledge base to check facts and rea- son about information. Knowledge in the form of natural language text is easy to ac- quire, but …...

AbstractQA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a …...

AbstractOur goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances …...

AbstractExtracting semantic relations from texts is a good way to build and supply a knowledge base, an indispensable resource for text analysis. We propose and evaluate the combination of three …...

AbstractBuilding knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search. The problem is very challenging and has been divided into …...

AbstractFine-grained entity typing aims at identifying the semantic type of an entity in KB. Type information is very important in knowledge bases, but are unfortunately incomplete even in some large …...

AbstractAnswer selection is an important but challenging task. Significant progresses have been made in domains where a large amount of labeled training data is available. However, obtaining rich annotated data …...

Directly adding the knowledge triples obtained from open information extraction systems into a knowledge base is often impractical due to a vocabulary gap between natural language (NL) expressions and knowledge …...

AbstractQuestion Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing …...

AbstractIn this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB …...

AbstractClassic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base. Conversely, sequence-to-sequence models learn to map dialogue …...

AbstractIn this paper, we study the problem of question answering over knowledge base. We identify that the primary bottleneck in this problem is the difficulty in accurately predicting the relations …...

Most of the existing Question Answer Systems focused on searching answers from the Knowledge-Base (KB), and ignore context aware information. Many Question Answer models perform well on public data-sets, but …...

AbstractWe present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation. We encode …...

AbstractDistant supervision has been widely used in relation extraction tasks without hand-labeled datasets recently. However, the automatically constructed datasets comprise numbers of wrongly labeled negative instances due to the incompleteness …...

AbstractRecent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base, such as …...

In this paper, we describe our efforts in estab- lishing a simple knowledge base by building a semantic network composed of concepts and word relationships in the context of disasters …...

AbstractIn this paper, we propose a simple method to predict salient locations from news article text using a knowledge base (KB). The proposed method uses a dictionary of locations created …...

AbstractIn this paper, we evaluate the progress of our field toward solving simple factoid questions over a knowledge base, a practically important problem in natural language interface to database. As …...

AbstractThe classic deep learning paradigm learns a model from the training data of a single task and the learned model is also tested on the same task. This paper studies …...

AbstractData augmentation techniques have been widely used to improve machine learning performance as they facilitate generalization. In this work, we propose a novel augmentation method to generate high quality synthetic …...

AbstractExisting end-to-end task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. To conquer these limitations, we propose …...

AbstractRecently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences. …...

AbstractDomain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain. In this paper, a novel joint training method is introduced …...

AbstractKnowledge bases are increasingly exploited as gold standard data sources which benefit various knowledge-driven NLP tasks. In this paper, we explore a new research direction to perform knowledge base (KB) …...

AbstractQuestion generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i.e. a set of triples. Two main challenges still face the current crop of encoder-decoder-based methods, …...

AbstractTask-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (KB) plays an essential role in …...

AbstractHumans do not make inferences over texts, but over models of what texts are about. When annotators are asked to annotate coreferent spans of text, it is therefore a somewhat …...

AbstractNowadays, fake news detection, which aims to verify whether a news document is trusted or fake, has become urgent and important. Most existing methods rely heavily on linguistic and semantic …...

End-to-End task-oriented dialogue systems generate responses based on dialog history and an accompanying knowledge base (KB). Infer- ring those KB entities that are most relevant for an utterance is crucial …...

AbstractIncorporating knowledge bases (KB) into end-to-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue …...

AbstractThe COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction …...

AbstractMeta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task. However, merely learning the knowledge from the historical tasks, adopted …...

AbstractIn entity linking, mentions of named entities in raw text are disambiguated against a knowledge base (KB). This work focuses on linking to unseen KBs that do not have training …...

Retrofitting is a technique used to move word vectors closer together or further apart in their space to reflect their relationships in a Knowl- edge Base (KB). However, retrofitting only …...

AbstractDistantly supervision automatically generates plenty of training samples for relation extraction. However, it also incurs two major problems: noisy labels and imbalanced training data. Previous works focus more on reducing …...

AbstractQuestion answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema linking. Conventional ranking-based KBQA …...

AbstractWe present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions. …...

AbstractComplex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints. Existing methods train one encoder-decoder-based model to fit all questions. …...

AbstractParsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or databases (DB). However, …...

AbstractNamed Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like CoNLL 2003 do not address many …...