Paper Group NANR 230
Together we stand: Siamese Networks for Similar Question Retrieval. Selection Criteria for Low Resource Language Programs. Remote Elicitation of Inflectional Paradigms to Seed Morphological Analysis in Low-Resource Languages. Extra-Specific Multiword Expressions for Language-Endowed Intelligent Agents. Opinion Holder and Target Extraction on Opinio …
Together we stand: Siamese Networks for Similar Question Retrieval
| Title | Together we stand: Siamese Networks for Similar Question Retrieval |
| Authors | Arpita Das, Harish Yenala, Manoj Chinnakotla, Manish Shrivastava |
| Abstract | |
| Tasks | Community Question Answering, Question Answering, Semantic Textual Similarity, Topic Models |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1036/ |
| https://www.aclweb.org/anthology/P16-1036 | |
| PWC | https://paperswithcode.com/paper/together-we-stand-siamese-networks-for |
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Selection Criteria for Low Resource Language Programs
| Title | Selection Criteria for Low Resource Language Programs |
| Authors | Christopher Cieri, Mike Maxwell, Stephanie Strassel, Jennifer Tracey |
| Abstract | This paper documents and describes the criteria used to select languages for study within programs that include low resource languages whether given that label or another similar one. It focuses on five US common task, Human Language Technology research and development programs in which the authors have provided information or consulting related to the choice of language. The paper does not describe the actual selection process which is the responsibility of program management and highly specific to a program{'}s individual goals and context. Instead it concentrates on the data and criteria that have been considered relevant previously with the thought that future program managers and their consultants may adapt these and apply them with different prioritization to future programs. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1720/ |
| https://www.aclweb.org/anthology/L16-1720 | |
| PWC | https://paperswithcode.com/paper/selection-criteria-for-low-resource-language |
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Remote Elicitation of Inflectional Paradigms to Seed Morphological Analysis in Low-Resource Languages
| Title | Remote Elicitation of Inflectional Paradigms to Seed Morphological Analysis in Low-Resource Languages |
| Authors | John Sylak-Glassman, Christo Kirov, David Yarowsky |
| Abstract | Structured, complete inflectional paradigm data exists for very few of the world{'}s languages, but is crucial to training morphological analysis tools. We present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language. Informants are tasked with completing language-specific paradigm elicitation templates. Templates are constructed by linguists using grammatical reference materials to ensure completeness. Each cell in a template is associated with contextual prompts designed to help informants with varying levels of linguistic expertise (from professional translators to untrained native speakers) provide the desired inflected form. To facilitate downstream use in interoperable NLP/HLT applications, each cell is also associated with a language-independent machine-readable set of morphological tags from the UniMorph Schema. This data is useful for seeding morphological analysis and generation software, particularly when the data is representative of the range of surface morphological variation in the language. At present, we have obtained 792 lemmas and 25,056 inflected forms from 15 languages. |
| Tasks | Morphological Analysis |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1497/ |
| https://www.aclweb.org/anthology/L16-1497 | |
| PWC | https://paperswithcode.com/paper/remote-elicitation-of-inflectional-paradigms |
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Extra-Specific Multiword Expressions for Language-Endowed Intelligent Agents
| Title | Extra-Specific Multiword Expressions for Language-Endowed Intelligent Agents |
| Authors | Marjorie McShane, Sergei Nirenburg |
| Abstract | Language-endowed intelligent agents benefit from leveraging lexical knowledge falling at different points along a spectrum of compositionality. This means that robust computational lexicons should include not only the compositional expectations of argument-taking words, but also non-compositional collocations (idioms), semi-compositional collocations that might be difficult for an agent to interpret (e.g., standard metaphors), and even collocations that could be compositionally analyzed but are so frequently encountered that recording their meaning increases the efficiency of interpretation. In this paper we argue that yet another type of string-to-meaning mapping can also be useful to intelligent agents: remembered semantic analyses of actual text inputs. These can be viewed as super-specific multi-word expressions whose recorded interpretations mimic a person{'}s memories of knowledge previously learned from language input. These differ from typical annotated corpora in two ways. First, they provide a full, context-sensitive semantic interpretation rather than select features. Second, they are are formulated in the ontologically-grounded metalanguage used in a particular agent environment, meaning that the interpretations contribute to the dynamically evolving cognitive capabilites of agents configured in that environment. |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3805/ |
| https://www.aclweb.org/anthology/W16-3805 | |
| PWC | https://paperswithcode.com/paper/extra-specific-multiword-expressions-for |
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Opinion Holder and Target Extraction on Opinion Compounds â A Linguistic Approach
| Title | Opinion Holder and Target Extraction on Opinion Compounds â A Linguistic Approach |
| Authors | Michael Wiegand, Christine Bocionek, Josef Ruppenhofer |
| Abstract | |
| Tasks | Question Answering, Sentiment Analysis |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/papers/N16-1094/n16-1094 |
| https://www.aclweb.org/anthology/N16-1094 | |
| PWC | https://paperswithcode.com/paper/opinion-holder-and-target-extraction-on |
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Phrase Representations for Multiword Expressions
| Title | Phrase Representations for Multiword Expressions |
| Authors | JoÄl Legrand, Ronan Collobert |
| Abstract | |
| Tasks | Chunking, Named Entity Recognition, Part-Of-Speech Tagging, Semantic Role Labeling, Word Embeddings |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/papers/W16-1810/w16-1810 |
| https://www.aclweb.org/anthology/W16-1810 | |
| PWC | https://paperswithcode.com/paper/phrase-representations-for-multiword |
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Situation entity types: automatic classification of clause-level aspect
| Title | Situation entity types: automatic classification of clause-level aspect |
| Authors | Annemarie Friedrich, Alexis Palmer, Manfred Pinkal |
| Abstract | |
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| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1166/ |
| https://www.aclweb.org/anthology/P16-1166 | |
| PWC | https://paperswithcode.com/paper/situation-entity-types-automatic |
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Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis
| Title | Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis |
| Authors | Shen Huang, Houfeng Wang |
| Abstract | Grammatical Error Diagnosis for Chinese has always been a challenge for both foreign learners and NLP researchers, for the variousity of grammar and the flexibility of expression. In this paper, we present a model based on Bidirectional Long Short-Term Memory(Bi-LSTM) neural networks, which treats the task as a sequence labeling problem, so as to detect Chinese grammatical errors, to identify the error types and to locate the error positions. In the corpora of this year{'}s shared task, there can be multiple errors in a single offset of a sentence, to address which, we simutaneously train three Bi-LSTM models sharing word embeddings which label Missing, Redundant and Selection errors respectively. We regard word ordering error as a special kind of word selection error which is longer during training phase, and then separate them by length during testing phase. In NLP-TEA 3 shared task for Chinese Grammatical Error Diagnosis(CGED), Our system achieved relatively high F1 for all the three levels in the traditional Chinese track and for the detection level in the Simpified Chinese track. |
| Tasks | Grammatical Error Detection, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4919/ |
| https://www.aclweb.org/anthology/W16-4919 | |
| PWC | https://paperswithcode.com/paper/bi-lstm-neural-networks-for-chinese |
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