[PDF] Top 20 Active Learning for Statistical Natural Language Parsing
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Active Learning for Statistical Natural Language Parsing
... We have examined three entropy-based uncertainty scores to measure the “usefulness” of a sample to im- proving a statistical model. We also define a distance for sentences of natural languages. Based on ... See full document
8
Modern Natural Language Interfaces to Databases: Composing Statistical Parsing with Semantic Tractability
... There are many reasons for the limited success of past NLI efforts (Androutsopoulos et al., 1995). We highlight several problems that are remedied by our approach. First, manually authoring and tuning a se- mantic ... See full document
7
A Fully Statistical Approach to Natural Language Interfaces
... Conclusion We have presented a fully trained statistical natural language interface system, with separate models corresponding to the classical processing steps of parsing, semantic inte[r] ... See full document
7
Joint Concept Learning and Semantic Parsing from Natural Language Explanations
... Natural language constitutes a predomi- nant medium for much of human learn- ing and ...concept learning from natural lan- guage explanations, and a small number of labeled examples of the ... See full document
10
A Second Order Joint Eisner Model for Syntactic and Semantic Dependency Parsing
... Jan Hajiˇc, Massimiliano Ciaramita, Richard Johans- son, Daisuke Kawahara, Maria Ant`onia Mart´ı, Llu´ıs M`arquez, Adam Meyers, Joakim Nivre, Sebastian Pad´o, Jan ˇStˇep´anek, Pavel Straˇn´ak, Mihai Surdeanu, Nianwen ... See full document
6
Corpus Variation and Parser Performance
... of natural language parsing, through the use of statistical methods trained using large corpora of hand-parsed training ...quantitative parsing results have been reported on other ... See full document
6
Discriminative Reranking for Natural Language Parsing
... baseline statistical parser is used to generate N-best output both for its training set and for test data ...of learning how to combine these different sources of ... See full document
46
Learning for Semantic Parsing with Statistical Machine Translation
... frame. Learning methods have been devised that can gen- erate MRs with a complex, nested structure ...deterministic parsing (Zelle and Mooney, 1996; Kate et ...in statistical NLP. Other ... See full document
8
Phrase Based Statistical Language Generation Using Graphical Models and Active Learning
... B AGEL uses a stack-based semantic representa- tion to constrain the sequence of semantic con- cepts to be searched. This representation can be seen as a linearised semantic tree similar to the one previously used for ... See full document
10
Predicting Academic Performance based on Social Activities
... online learning environment), finding patterns in student behavior, displaying relevant information in suggestive formats; the end goal is the prediction of student performance, the optimization of the educational ... See full document
5
Active Learning for New Domains in Natural Language Understanding
... We compare the efficacy of least- confidence (Lewis and Catlett, 1994) and query-by-committee (Freund et al., 1997) AL for new domains. Moreover, we propose an AL algorithm called Majority-CRF, designed to improve both ... See full document
7
Learning Structured Natural Language Representations for Semantic Parsing
... algorithm uses a buffer to store input tokens in the utterance and a stack to store partially com- pleted trees. A major difference in our semantic parsing scenario is that tokens in the buffer are not fetched in ... See full document
12
Diversity in Spectral Learning for Natural Language Parsing
... We describe an approach to create a di- verse set of predictions with spectral learn- ing of latent-variable PCFGs (L-PCFGs). Our approach works by creating multiple spectral models where noise is added to the underlying ... See full document
11
Head Driven Statistical Models for Natural Language Parsing
... In the case of PCFGs, this can be accomplished using a variant of the CKY algorithm applied to weighted grammars (providing that the PCFG can be converted to an equiv- alent PCFG in Chomsky normal form); see, for ... See full document
49
Analysis of Statistical Parsing in Natural Language Processing
... Part-of-speech tagging is the process of assigning a part-of-speech (such as a noun, verb, pronoun, preposition, adverb, and adjective), to each word in a sentence. The input to a tagging algorithm is the sequence of ... See full document
6
A Survey of Natural Language Query Builder Interface for Structured Databases using Dependency Parsing
... their parsing are graph-based ...the learning stage, the set of parameters of this function is ...the parsing stage, the graph that maximizes the score given by this function is built, which ... See full document
6
Active learning for deep semantic parsing
... “overnight” active learning are nearly as good as traditional active learning, showing in similar area under the curve value in Figure ... See full document
6
The ERG at MRP 2019: Radically Compositional Semantic Dependencies
... for parsing and ...combining statistical PoS tagging and on-the-fly lexical instantiation for ‘standard’ open- class words ...2017). Parsing times for these data sets measure in seconds per sentence, ... See full document
5
Compositional pre training for neural semantic parsing
... Semantic parsing is the process of translating natural language utterances into logical forms, which has many important applications such as question answering and instruction follow- ...the ... See full document
7
Statistical language learning
... " or "can participants learn this structure?" progressively loose interest in favour of questions such as "given several potential cues and interpretations available, under what specific[r] ... See full document
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