Abstract | Learning a semantic lexicon is often an important first step in building a system that learns to interpret the meaning of natural language . |
Background | Formally, the system is given training data in the form: {(el,a1,w1),(eg,a2,w2),...,(en,an,wn)}, where 67; is a written natural language instruction, at, is an observed action sequence, and w, is a description of the virtual world. |
Collecting Additional Data with Mechanical Turk | There are two types of data we are interested in collecting: natural language navigation instructions and follower data. |
Experiments | It verified that we are indeed collecting useful information and that non-experts are fully capable of training the system by demonstrating how to use natural language in relevant contexts. |
Introduction | Learning to understand the semantics of human languages has been one of the ultimate goals of natural language processing (NLP). |
Introduction | Traditional learning approaches have relied on access to parallel corpora of natural language sentences paired with their meanings (Mooney, 2007; Zettlemoyer and Collins, 2007; Lu et al., 2008; Kwiatkowski et al., 2010). |
Introduction | By using a MRG that correlates better to the structure of natural language , we further improve the performance on the navigation task. |
Online Lexicon Learning Algorithm | KRISP learns string-kernel classifiers that maps natural language substrings to MRG production rules. |
Online Lexicon Learning Algorithm | Consequently, it is important that the production rules in the MRG mirror the structure of natural language (Kate, 2008). |
Online Lexicon Learning Algorithm | While these rules are quite expressive, they often do not correspond well to any words or phrases in natural language . |
Abstract | However, in natural language , some facts are implicit, and identifying them requires “reading between the lines”. |
Abstract | It involves learning uncertain commonsense knowledge (in the form of probabilistic first-order rules) from natural language text by mining a large corpus of automatically extracted facts. |
Conclusions | We have introduced a novel approach using Bayesian Logic Programs to learn to infer implicit information from facts extracted from natural language text. |
Introduction | To the best of our knowledge, this is the first paper that employs BLPs for inferring implicit information from natural language text. |
Introduction | We demonstrate that it is possible to learn the structure and the parameters of BLPs automatically using only noisy extractions from natural language text, which we then use to infer additional facts from text. |
Learning BLPs to Infer Implicit Facts | In our task, the supervised training data consists of facts that are extracted from the natural language text. |
Abstract | We describe a joint model for understanding user actions in natural language utterances. |
Abstract | We inject information extracted from unstructured web search query logs as prior information to enhance the generative process of the natural language utterance understanding model. |
Background | However data sources in VPA systems pose new challenges, such as variability and ambiguities in natural language , or short utterances that rarely contain contextual information, etc. |
Conclusions | Experimental results using the new Bayesian model indicate that we can effectively learn and discover meta-aspects in natural language utterances, outperforming the supervised baselines, especially when there are fewer labeled and more unlabeled utterances. |
Introduction | The contributions of this paper are as follows: (i) construction of a novel Bayesian framework for semantic parsing of natural language (NL) utterances in a unifying framework in §4, (ii) representation of seed labeled data and information from web queries as informative prior to design a novel utterance understanding model in §3 & §4, (iii) comparison of our results to supervised sequential and joint learning methods on NL utterances in §5. |
Abstract | Our proposal performs distant supervised learning to extract a set of relations from a natural language corpus, and anchors each of them to an interval of temporal validity, aggregating evidence from documents supporting the relation. |
Conclusions | This paper introduces the problem of extracting, from unrestricted natural language text, relational knowledge anchored to a temporal span, aggregating temporal evidence from a collection of documents. |
Introduction | The Temporally anchored relation extraction problem consists in, given a natural language text document corpus, C, a target entity, 6, and a target |
Related Work | their relation to events in natural language , the complete problem of temporally anchored relation extraction remains relatively unexplored. |
Temporal Anchors | This sharp temporal interval fails to capture the imprecision of temporal boundaries conveyed in natural language text. |
Abstract | We present a holistic data—driven approach to image description generation, exploiting the vast amount of (noisy) parallel image data and associated natural language descriptions available on the web. |
Introduction | Automatically describing images in natural language is an intriguing, but complex AI task, requiring accurate computational visual recognition, comprehensive world knowledge, and natural language generation. |
Introduction | By judiciously exploiting the correspondence between image content elements and phrases, it is possible to generate natural language descriptions that are substantially richer in content and more linguistically interesting than previous work. |
Vision & Phrase Retrieval | For a query image, we retrieve relevant candidate natural language phrases by visually comparing the query image to database images from the SBU Captioned Photo Collection (Ordonez et al., 2011) (1 million photographs with associated human-composed descriptions). |
Abstract | We describe all the linguistic resources and natural language processing techniques that we have exploited for this task. |
Introduction | naming agencies and naive generators) that can be used for obtaining name suggestions, we propose a system which combines several linguistic resources and natural language processing (NLP) techniques to generate creative names, more specifically neologisms based on homophonic puns and metaphors. |
System Description | Accordingly, we have made a systematic attempt to replicate these processes, and implemented a system which combines methods and resources used in various areas of Natural Language Processing (NLP) to create neologisms based on homophonic puns and metaphors. |
System Description | This resource consists of nodes representing concepts which are in the form of words or short phrases of natural language , and labeled relations between them. |
Experimental Design | and Collins (2007) instead, which combines the utterances of a single user in one scenario and contains 5,426 scenarios in total; each scenario corresponds to a (manually annotated) formal meaning representation (it-expression) and its translation in natural language . |
Introduction | Here, the records provide a structured representation of the flight details (e. g., departure and arrival time, location), and the text renders some of this information in natural language . |
Introduction | Specifically, we define a probabilistic context-free grammar (PCFG) that captures the structure of the database and its correspondence to natural language . |
Introduction | However, the study of the phenomenon in natural language is relatively nascent, and its incorporation into language technologies is almost nonexistent. |
Introduction | Moreover, applications of natural language processing generally lack the ability to recognize and interpret metalanguage (Anderson et al. |
Introduction | Applications of natural language understanding cannot process metalanguage without detecting it, especially when upstream components (such as parsers) mangle its structure. |
Evaluations | Three graduate students in natural language processing annotate intruding paths. |
Experiments | Following (Yao et al., 2011), we filter out noisy documents and use natural language packages to annotate the documents, including NER tagging (Finkel et al., 2005) and dependency parsing (Nivre et al., 2004). |
Introduction | Here, the relation extractor simultaneously discovers facts expressed in natural language , and the ontology into which they are assigned. |