title stringlengths 5 342 | author stringlengths 3 2.17k | year int64 1.95k 2.02k | abstract stringlengths 0 12.7k | pages stringlengths 1 702 | queryID stringlengths 4 40 | query stringlengths 1 300 | paperID stringlengths 0 40 | include int64 0 1 |
|---|---|---|---|---|---|---|---|---|
Embedding Methods for Fine Grained Entity Type Classification | Yogatama, Dani and
Gillick, Daniel and
Lazic, Nevena | 2,015 | nan | 291--296 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | cd51e6faf377104269ba1e905ce430650677155c | 1 |
Feature-Rich Part-Of-Speech Tagging Using Deep Syntactic and Semantic Analysis | Jackov, Luchezar | 2,015 | nan | 224--231 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 6c27573c00e04f9956c6ccc38fac8fc753267161 | 0 |
Improving Entity Linking through Semantic Reinforced Entity Embeddings | Hou, Feng and
Wang, Ruili and
He, Jun and
Zhou, Yi | 2,020 | Entity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity embeddings are learned from canonical Wikipedia articles and local contexts surrounding target entities. Such entity embeddings are effectiv... | 6843--6848 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 10108878e053d28d72f059d7ec9e4a15281dad96 | 1 |
Marking Trustworthiness with Near Synonyms: A Corpus-based Study of {``}Renwei{''} and {``}Yiwei{''} in {C}hinese | Li, Bei and
Huang, Chu-Ren and
Chen, Si | 2,020 | nan | 453--461 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | ef018aa1e8f465ab76e192d41c32c6c237cfeb31 | 0 |
{FINET}: Context-Aware Fine-Grained Named Entity Typing | Del Corro, Luciano and
Abujabal, Abdalghani and
Gemulla, Rainer and
Weikum, Gerhard | 2,015 | nan | 868--878 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 729698ea49c505771038cc84756ad4569f35e816 | 1 |
{WSD}-games: a Game-Theoretic Algorithm for Unsupervised Word Sense Disambiguation | Tripodi, Rocco and
Pelillo, Marcello | 2,015 | nan | 329--334 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8b25ca2fcceb3ad47e3a552d122e25c841088676 | 0 |
{MZET}: Memory Augmented Zero-Shot Fine-grained Named Entity Typing | Zhang, Tao and
Xia, Congying and
Lu, Chun-Ta and
Yu, Philip | 2,020 | Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types. However, with the growing size and granularity of the entity types, few previous researches concern with newly emerged entity types. In this paper, we propose MZET, a novel memory augmented FNET (... | 77--87 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 564693e8f95ea1046f567f73715a838900289c3f | 1 |
Incremental Neural Lexical Coherence Modeling | Jeon, Sungho and
Strube, Michael | 2,020 | Pretrained language models, neural models pretrained on massive amounts of data, have established the state of the art in a range of NLP tasks. They are based on a modern machine-learning technique, the Transformer which relates all items simultaneously to capture semantic relations in sequences. However, it differs fr... | 6752--6758 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8f70089de702d5da30e600ae53d35bc1580381cb | 0 |
{HYENA}: Hierarchical Type Classification for Entity Names | Yosef, Mohamed Amir and
Bauer, Sandro and
Hoffart, Johannes and
Spaniol, Marc and
Weikum, Gerhard | 2,012 | nan | 1361--1370 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | bcaef36e362c84c5b492425880e85f1ac781c661 | 1 |
Employing Compositional Semantics and Discourse Consistency in {C}hinese Event Extraction | Li, Peifeng and
Zhou, Guodong and
Zhu, Qiaoming and
Hou, Libin | 2,012 | nan | 1006--1016 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f00b7db20c7b65292c4536cc82ad6bdb8e4afd04 | 0 |
Extended Named Entity Ontology with Attribute Information | Sekine, Satoshi | 2,008 | Named Entities (NE) are regarded as an important type of semantic knowledge in many natural language processing (NLP) applications. Originally, a limited number of NE categories were proposed. In MUC, it was 7 categories - people, organization, location, time, date, money and percentage expressions. However, it was not... | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 24424f4050700dfa940851385d2e1ab7ba5d0cdc | 1 |
Latent Morpho-Semantic Analysis: Multilingual Information Retrieval with Character N-Grams and Mutual Information | Chew, Peter A. and
Bader, Brett W. and
Abdelali, Ahmed | 2,008 | nan | 129--136 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 89677da2c13fc1647ed1ade5aecaa8a40d9002b2 | 0 |
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model | Dai, Hongliang and
Song, Yangqiu and
Wang, Haixun | 2,021 | Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions. A key challenge for this ultra-fine entity typing task is that human annotated data are extremely scarce,... | 1790--1799 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 70b49a024787d3ad374fb78dc87e3ba2b5e16566 | 1 |
Optimizing {NLU} Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems | Wang, Tong and
Chen, Jiangning and
Malmir, Mohsen and
Dong, Shuyan and
He, Xin and
Wang, Han and
Su, Chengwei and
Liu, Yue and
Liu, Yang | 2,021 | In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved. This may result in intent classification and slot tagging errors. In this work, we propose to leverage Entit... | 19--25 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 32e501a0cd9a4ebcaa5989657690be38b8340340 | 0 |
Modeling Fine-Grained Entity Types with Box Embeddings | Onoe, Yasumasa and
Boratko, Michael and
McCallum, Andrew and
Durrett, Greg | 2,021 | Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types{'} complex interdependencies. We study the ability of box embeddings, which embed concepts as d-dimensional hyperrectangles, to capture hierarchie... | 2051--2064 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 176e3cbe3141c8b874df663711dca9b7470b8243 | 1 |
{LGESQL}: Line Graph Enhanced Text-to-{SQL} Model with Mixed Local and Non-Local Relations | Cao, Ruisheng and
Chen, Lu and
Chen, Zhi and
Zhao, Yanbin and
Zhu, Su and
Yu, Kai | 2,021 | This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task. Previous methods are typically node-centric and merely utilize different weight matrices to parameterize edge types, which 1) ignore the rich semantics embedded in the topological structure of edges, and 2) fail to di... | 2541--2555 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 50db74aa7e662b640ccbf37788af62cd8af3e930 | 0 |
A {C}hinese Corpus for Fine-grained Entity Typing | Lee, Chin and
Dai, Hongliang and
Song, Yangqiu and
Li, Xin | 2,020 | Fine-grained entity typing is a challenging task with wide applications. However, most existing datasets for this task are in English. In this paper, we introduce a corpus for Chinese fine-grained entity typing that contains 4,800 mentions manually labeled through crowdsourcing. Each mention is annotated with free-form... | 4451--4457 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 853986783fdc27c7cebb04ba638dd7fe48c5de23 | 1 |
{``}What Do You Mean by That?{''} A Parser-Independent Interactive Approach for Enhancing Text-to-{SQL} | Li, Yuntao and
Chen, Bei and
Liu, Qian and
Gao, Yan and
Lou, Jian-Guang and
Zhang, Yan and
Zhang, Dongmei | 2,020 | In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in this area has been made recently, most parsers may fall short when they are deployed in real systems. One main reason stems from the difficul... | 6913--6922 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | bc247abf8180f583a42de392e4f7d2b2a41ad72d | 0 |
Fine-grained Named Entity Annotations for {G}erman Biographic Interviews | Ruppenhofer, Josef and
Rehbein, Ines and
Flinz, Carolina | 2,020 | We present a fine-grained NER annotations with 30 labels and apply it to German data. Building on the OntoNotes 5.0 NER inventory, our scheme is adapted for a corpus of transcripts of biographic interviews by adding categories for AGE and LAN(guage) and also features extended numeric and temporal categories. Applying t... | 4605--4614 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8122242e40d95b288cfbe14024988f41fd17ab6b | 1 |
Collocations in {R}ussian Lexicography and {R}ussian Collocations Database | Khokhlova, Maria | 2,020 | The paper presents the issue of collocability and collocations in Russian and gives a survey of a wide range of dictionaries both printed and online ones that describe collocations. Our project deals with building a database that will include dictionary and statistical collocations. The former can be described in vario... | 3198--3206 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 9780480a952edddef523c98c2ba0f500a572ad46 | 0 |
{ENTYFI}: A System for Fine-grained Entity Typing in Fictional Texts | Chu, Cuong Xuan and
Razniewski, Simon and
Weikum, Gerhard | 2,020 | Fiction and fantasy are archetypes of long-tail domains that lack suitable NLP methodologies and tools. We present ENTYFI, a web-based system for fine-grained typing of entity mentions in fictional texts. It builds on 205 automatically induced high-quality type systems for popular fictional domains, and provides recomm... | 100--106 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 2d2eaf2a13c50f49bc3a1842581a1b9dc8c1ffc3 | 1 |
{S}eg{B}o: A Database of Borrowed Sounds in the World{'}s Languages | Grossman, Eitan and
Eisen, Elad and
Nikolaev, Dmitry and
Moran, Steven | 2,020 | Phonological segment borrowing is a process through which languages acquire new contrastive speech sounds as the result of borrowing new words from other languages. Despite the fact that phonological segment borrowing is documented in many of the world{'}s languages, to date there has been no large-scale quantitative s... | 5316--5322 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 82b05cfaab8691236c88fa388b3477d06f108819 | 0 |
Description-Based Zero-shot Fine-Grained Entity Typing | Obeidat, Rasha and
Fern, Xiaoli and
Shahbazi, Hamed and
Tadepalli, Prasad | 2,019 | Fine-grained Entity typing (FGET) is the task of assigning a fine-grained type from a hierarchy to entity mentions in the text. As the taxonomy of types evolves continuously, it is desirable for an entity typing system to be able to recognize novel types without additional training. This work proposes a zero-shot entit... | 807--814 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 51b958dd76a6aefcd521ec0f503c3e334f711362 | 1 |
Continuous Quality Control and Advanced Text Segment Annotation with {WAT}-{SL} 2.0 | Lohr, Christina and
Kiesel, Johannes and
Luther, Stephanie and
Hellrich, Johannes and
Kolditz, Tobias and
Stein, Benno and
Hahn, Udo | 2,019 | Today{'}s widely used annotation tools were designed for annotating typically short textual mentions of entities or relations, making their interface cumbersome to use for long(er) stretches of text, e.g, sentences running over several lines in a document. They also lack systematic support for hierarchically structured... | 215--219 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 1c404bcaf18e749a450578daf322f79f82a4e949 | 0 |
Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds | Zhang, Sheng and
Duh, Kevin and
Van Durme, Benjamin | 2,018 | Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions. We propose a neural architecture which learns a distributional semantic representation that leverages a greater amount of semantic context {--} both document and sentence level information {--} than prior work. We find t... | 173--179 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 87abde0432f4377aed50ade6fb49299d4bd018bb | 1 |
{AMR} dependency parsing with a typed semantic algebra | Groschwitz, Jonas and
Lindemann, Matthias and
Fowlie, Meaghan and
Johnson, Mark and
Koller, Alexander | 2,018 | We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system.... | 1831--1841 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 25109699b098c786832c906e4b36fa76fb2b66a0 | 0 |
Ultra-Fine Entity Typing | Choi, Eunsol and
Levy, Omer and
Choi, Yejin and
Zettlemoyer, Luke | 2,018 | We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity. This formulation allows us to use a new type of distant supervision at large scale: head wor... | 87--96 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 4157834ed2d2fea6b6f652a72a9d0487edbc9f57 | 1 |
Aggression Identification Using Deep Learning and Data Augmentation | Risch, Julian and
Krestel, Ralf | 2,018 | Social media platforms allow users to share and discuss their opinions online. However, a minority of user posts is aggressive, thereby hinders respectful discussion, and {---} at an extreme level {---} is liable to prosecution. The automatic identification of such harmful posts is important, because it can support the... | 150--158 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f2a9d16e852e6008b11244df899672231efb7a12 | 0 |
Improving Entity Linking by Modeling Latent Relations between Mentions | Le, Phong and
Titov, Ivan | 2,018 | Entity linking involves aligning textual mentions of named entities to their corresponding entries in a knowledge base. Entity linking systems often exploit relations between textual mentions in a document (e.g., coreference) to decide if the linking decisions are compatible. Unlike previous approaches, which relied on... | 1595--1604 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 44b18e054bc0ef6e9afe04732807a1f38d002179 | 1 |
A Hybrid Approach to Automatic Corpus Generation for {C}hinese Spelling Check | Wang, Dingmin and
Song, Yan and
Li, Jing and
Han, Jialong and
Zhang, Haisong | 2,018 | Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing(NLP) applications, but also facilitates reading and understanding of running texts in peoples{'} daily lives. However, to utilize data-driven approaches for CSC, there is one m... | 2517--2527 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | c12e270f347334ced34614e110b9319888522da8 | 0 |
Building Language Models for Text with Named Entities | Parvez, Md Rizwan and
Chakraborty, Saikat and
Ray, Baishakhi and
Chang, Kai-Wei | 2,018 | Text in many domains involves a significant amount of named entities. Predicting the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a language model which can learn the entity names by lever... | 2373--2383 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 6e618c1be08cecd8d71fe65512ad44814c650ffc | 1 |
Analogical Reasoning on {C}hinese Morphological and Semantic Relations | Li, Shen and
Zhao, Zhe and
Hu, Renfen and
Li, Wensi and
Liu, Tao and
Du, Xiaoyong | 2,018 | Analogical reasoning is effective in capturing linguistic regularities. This paper proposes an analogical reasoning task on Chinese. After delving into Chinese lexical knowledge, we sketch 68 implicit morphological relations and 28 explicit semantic relations. A big and balanced dataset CA8 is then built for this task,... | 138--143 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 1f8c70ce22fc5b34ee725d79d4a061b3062f6fc5 | 0 |
Zero-Shot Open Entity Typing as Type-Compatible Grounding | Zhou, Ben and
Khashabi, Daniel and
Tsai, Chen-Tse and
Roth, Dan | 2,018 | The problem of entity-typing has been studied predominantly as a supervised learning problems, mostly with task-specific annotations (for coarse types) and sometimes with distant supervision (for fine types). While such approaches have strong performance within datasets they often lack the flexibility to transfer acros... | 2065--2076 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8456a5ed15b465e82bba3b974ff4e25c3b652826 | 1 |
Quantifying Qualitative Data for Understanding Controversial Issues | Wojatzki, Michael and
Mohammad, Saif and
Zesch, Torsten and
Kiritchenko, Svetlana | 2,018 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 7df3bca7de01f2e017feb46eb59d7232e2494439 | 0 |
An Empirical Study on Fine-Grained Named Entity Recognition | Mai, Khai and
Pham, Thai-Hoang and
Nguyen, Minh Trung and
Nguyen, Tuan Duc and
Bollegala, Danushka and
Sasano, Ryohei and
Sekine, Satoshi | 2,018 | Named entity recognition (NER) has attracted a substantial amount of research. Recently, several neural network-based models have been proposed and achieved high performance. However, there is little research on fine-grained NER (FG-NER), in which hundreds of named entity categories must be recognized, especially for n... | 711--722 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f0c39dd1715d0050168467a5afa22855d6d2fe2c | 1 |
A Fast and Flexible Webinterface for Dialect Research in the Low Countries | van Hout, Roeland and
van der Sijs, Nicoline and
Komen, Erwin and
van den Heuvel, Henk | 2,018 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | db91c269785a12b21c7b187112f2233a3897384e | 0 |
Fine-Grained Entity Typing with High-Multiplicity Assignments | Rabinovich, Maxim and
Klein, Dan | 2,017 | As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data source... | 330--334 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 1908e93bfa8ee6f1707a2513095e48945823727a | 1 |
{ECNU} at {S}em{E}val-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for {T}witter Message Polarity Classification | Zhou, Yunxiao and
Lan, Man and
Wu, Yuanbin | 2,017 | This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine lear... | 812--816 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 1b0cf0cededba48d2fea32cdcf407906c61cf14f | 0 |
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities | Yaghoobzadeh, Yadollah and
Sch{\"u}tze, Hinrich | 2,017 | Entities are essential elements of natural language. In this paper, we present methods for learning multi-level representations of entities on three complementary levels: character (character patterns in entity names extracted, e.g., by neural networks), word (embeddings of words in entity names) and entity (entity emb... | 578--589 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | bdeb6ff1a9607468af50609ccde1f55ce64b0ad4 | 1 |
Automatic classification of doctor-patient questions for a virtual patient record query task | Campillos Llanos, Leonardo and
Rosset, Sophie and
Zweigenbaum, Pierre | 2,017 | We present the work-in-progress of automating the classification of doctor-patient questions in the context of a simulated consultation with a virtual patient. We classify questions according to the computational strategy (rule-based or other) needed for looking up data in the clinical record. We compare {`}traditional... | 333--341 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 17e36e9193f8154a8fd2e5c6ac44b2c4ad22a6ed | 0 |
Noise Mitigation for Neural Entity Typing and Relation Extraction | Yaghoobzadeh, Yadollah and
Adel, Heike and
Sch{\"u}tze, Hinrich | 2,017 | In this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features. Our target tasks are entity typing and relation extraction. For the first noise type, we introduce multi-instance multi-label learning algorithms using neural n... | 1183--1194 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | b0b0c68c3457faa85ed3bbd3252ac65ba55da5c6 | 1 |
{LIPN}-{IIMAS} at {S}em{E}val-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity | Arroyo-Fern{\'a}ndez, Ignacio and
Meza Ruiz, Ivan Vladimir | 2,017 | In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word alignment approach, which is linguistically motivated. The neural approaches proposed herein are divide... | 208--212 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | a640fb4a11fc767f4bf801f7a7320b92efc807d3 | 0 |
Deep Joint Entity Disambiguation with Local Neural Attention | Ganea, Octavian-Eugen and
Hofmann, Thomas | 2,017 | We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combin... | 2619--2629 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | eead15f6cd00df5e1bd7733108695778c8d43240 | 1 |
Temporal Orientation of Tweets for Predicting Income of Users | Hasanuzzaman, Mohammed and
Kamila, Sabyasachi and
Kaur, Mandeep and
Saha, Sriparna and
Ekbal, Asif | 2,017 | Automatically estimating a user{'}s socio-economic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive... | 659--665 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 9bc68cf51f15af853694f63cbf01dd7051685cc2 | 0 |
Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods | Neelakantan, Arvind and
Chang, Ming-Wei | 2,015 | nan | 515--525 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 4e278a0fe9fbfeceb29acde435706aa790aeda56 | 1 |
{CUNI} in {WMT}15: Chimera Strikes Again | Bojar, Ond{\v{r}}ej and
Tamchyna, Ale{\v{s}} | 2,015 | nan | 79--83 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | b95c8e996b37d3dc81e29e44b2adde23bfb4d951 | 0 |
Corpus-level Fine-grained Entity Typing Using Contextual Information | Yaghoobzadeh, Yadollah and
Sch{\"u}tze, Hinrich | 2,015 | nan | 715--725 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8b298ce5f81c5ffd63f5c5ab3634dbfd350a92e4 | 1 |
Lost in Discussion? Tracking Opinion Groups in Complex Political Discussions by the Example of the {FOMC} Meeting Transcriptions | Zirn, C{\"a}cilia and
Meusel, Robert and
Stuckenschmidt, Heiner | 2,015 | nan | 747--753 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8a5b7bba4fa1ce57009fadacd77f9b8656b35bab | 0 |
Incremental Joint Extraction of Entity Mentions and Relations | Li, Qi and
Ji, Heng | 2,014 | nan | 402--412 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8b156bdce947783b8c7071f02557b414ab7b5276 | 1 |
{HBB}4{ALL}: media accessibility for {HBB} {TV} | nan | 2,014 | nan | 127 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 88a0a281e5b95b608d75ab0b786006fc9ed8575f | 0 |
A Convolutional Neural Network for Modelling Sentences | Kalchbrenner, Nal and
Grefenstette, Edward and
Blunsom, Phil | 2,014 | nan | 655--665 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 27725a2d2a8cee9bf9fffc6c2167017103aba0fa | 1 |
Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment | Belinkov, Yonatan and
Lei, Tao and
Barzilay, Regina and
Globerson, Amir | 2,014 | Prepositional phrase (PP) attachment disambiguation is a known challenge in syntactic parsing. The lexical sparsity associated with PP attachments motivates research in word representations that can capture pertinent syntactic and semantic features of the word. One promising solution is to use word vectors induced from... | 561--572 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 18f648bb494c87f9cf9fe7db744aa233de9313c1 | 0 |
Fine-grained Semantic Typing of Emerging Entities | Nakashole, Ndapandula and
Tylenda, Tomasz and
Weikum, Gerhard | 2,013 | nan | 1488--1497 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 6629785cb5c9c96921f97e7a8c56dbe63f80d9ef | 1 |
A User Study: Technology to Increase Teachers{'} Linguistic Awareness to Improve Instructional Language Support for {E}nglish Language Learners | Burstein, Jill and
Sabatini, John and
Shore, Jane and
Moulder, Brad and
Lentini, Jennifer | 2,013 | nan | 1--10 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 77727365299413c51d85a0a7848bbcbbcce824d4 | 0 |
Multi-instance Multi-label Learning for Relation Extraction | Surdeanu, Mihai and
Tibshirani, Julie and
Nallapati, Ramesh and
Manning, Christopher D. | 2,012 | nan | 455--465 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | fbe358ce706371b93c10c4395cab9a78ad3aef67 | 1 |
Classification of Interviews - A Case Study on Cancer Patients | Patra, Braja Gopal and
Kundu, Amitava and
Das, Dipankar and
Bandyopadhyay, Sivaji | 2,012 | nan | 27--36 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | eed4d404a91f803a8f408b22a5ddf338b59ba7bc | 0 |
{PATTY}: A Taxonomy of Relational Patterns with Semantic Types | Nakashole, Ndapandula and
Weikum, Gerhard and
Suchanek, Fabian | 2,012 | nan | 1135--1145 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | b162c99873c929447bb7ff48d454867aa83f375c | 1 |
Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition | Li, Ying and
Fung, Pascale | 2,012 | nan | 1671--1680 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 97e0304db883c30393534adc5dea2c891b50280c | 0 |
Class Label Enhancement via Related Instances | Kozareva, Zornitsa and
Voevodski, Konstantin and
Teng, Shanghua | 2,011 | nan | 118--128 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 791031c4af681f032175a35b935194fe0ac26534 | 1 |
The Semi-Automatic Construction of Part-Of-Speech Taggers for Specific Languages by Statistical Methods | Yamasaki, Tomohiro and
Wakaki, Hiromi and
Suzuki, Masaru | 2,011 | nan | 23--29 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 4212340339fff0148d774caae05221c686b4d1ea | 0 |
Robust Disambiguation of Named Entities in Text | Hoffart, Johannes and
Yosef, Mohamed Amir and
Bordino, Ilaria and
F{\"u}rstenau, Hagen and
Pinkal, Manfred and
Spaniol, Marc and
Taneva, Bilyana and
Thater, Stefan and
Weikum, Gerhard | 2,011 | nan | 782--792 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | d95738f38d97a030d98508357e4d5c78a4a208ba | 1 |
Using a {W}ikipedia-based Semantic Relatedness Measure for Document Clustering | Yazdani, Majid and
Popescu-Belis, Andrei | 2,011 | nan | 29--36 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | da1e1ee70d3be350ec1ceb70fc1de34048dc0c33 | 0 |
Identifying Relations for Open Information Extraction | Fader, Anthony and
Soderland, Stephen and
Etzioni, Oren | 2,011 | nan | 1535--1545 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | d4b651d6a904f69f8fa1dcad4ebe972296af3a9a | 1 |
Query Weighting for Ranking Model Adaptation | Cai, Peng and
Gao, Wei and
Zhou, Aoying and
Wong, Kam-Fai | 2,011 | nan | 112--122 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 281c587dddbda1ad32f7566d44d18c5f771e5cb2 | 0 |
Inducing Fine-Grained Semantic Classes via Hierarchical and Collective Classification | Rahman, Altaf and
Ng, Vincent | 2,010 | nan | 931--939 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 184b5d6fd0ec7b94b815ca18227fa00d9a6b58b1 | 1 |
Streaming First Story Detection with application to {T}witter | Petrovi{\'c}, Sa{\v{s}}a and
Osborne, Miles and
Lavrenko, Victor | 2,010 | nan | 181--189 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8854ca5546396ef225112ec828094882a71fd01e | 0 |
{W}iki{S}ense: Supersense Tagging of {W}ikipedia Named Entities Based {W}ord{N}et | Chang, Joseph and
Tsai, Richard Tzong-Han and
Chang, Jason S. | 2,009 | nan | 72--81 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 559e2679ccb23f722b262410c32bab131214bbae | 1 |
The Construction of a {C}hinese-{E}nglish Patent Parallel Corpus | Lu, Bin and
Tsou, Benjamin K. and
Zhu, Jingbo and
Jiang, Tao and
Kwong, Oi Yee | 2,009 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | ab6c0ef09337c398aa12eaf93805b706b0fb2ed9 | 0 |
{W}eb-Scale Distributional Similarity and Entity Set Expansion | Pantel, Patrick and
Crestan, Eric and
Borkovsky, Arkady and
Popescu, Ana-Maria and
Vyas, Vishnu | 2,009 | nan | 938--947 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 00fce98c3fda59bcb84b6d0626fb3137d2fbb984 | 1 |
k-{N}earest Neighbor {M}onte-{C}arlo Control Algorithm for {POMDP}-Based Dialogue Systems | Lef{\`e}vre, Fabrice and
Ga{\v{s}}i{\'c}, Milica and
Jur{\v{c}}{\'\i}{\v{c}}ek, Filip and
Keizer, Simon and
Mairesse, Fran{\c{c}}ois and
Thomson, Blaise and
Yu, Kai and
Young, Steve | 2,009 | nan | 272--275 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 5367ae4fd4dbb8c21b8c7f083d434a7f69d0577e | 0 |
Distributed Word Clustering for Large Scale Class-Based Language Modeling in Machine Translation | Uszkoreit, Jakob and
Brants, Thorsten | 2,008 | nan | 755--762 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 00ae51ba9340abc30d36804f9b51ab83b81cec23 | 1 |
Revisiting the Impact of Different Annotation Schemes on {PCFG} Parsing: A Grammatical Dependency Evaluation | Boyd, Adriane and
Meurers, Detmar | 2,008 | nan | 24--32 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 8d616d33bddd764960280936e40ceb0cbbd0e60c | 0 |
Weakly-Supervised Acquisition of Labeled Class Instances using Graph Random Walks | Talukdar, Partha Pratim and
Reisinger, Joseph and
Pa{\c{s}}ca, Marius and
Ravichandran, Deepak and
Bhagat, Rahul and
Pereira, Fernando | 2,008 | nan | 582--590 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | eca6dfe0a741b52db388e04febf71f542353a63c | 1 |
Semantic Frame Annotation on the {F}rench {MEDIA} corpus | Meurs, Marie-Jean and
Duvert, Fr{\'e}d{\'e}ric and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Lef{\`e}vre, Fabrice and
de Mori, Renato | 2,008 | This paper introduces a knowledge representation formalism used for annotation of the French MEDIA dialogue corpus in terms of high level semantic structures. The semantic annotation, worked out according to the Berkeley FrameNet paradigm, is incremental and partially automated. We describe an automatic interpretation ... | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 175b20b24dc4f7980c756fd24541ffb5e2a1533b | 0 |
Question Classification using Head Words and their Hypernyms | Huang, Zhiheng and
Thint, Marcus and
Qin, Zengchang | 2,008 | nan | 927--936 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 94a9af119df61f501980cf095700f35c2a7762a3 | 1 |
Entailment-based Question Answering for Structured Data | Sacaleanu, Bogdan and
Orasan, Constantin and
Spurk, Christian and
Ou, Shiyan and
Ferrandez, Oscar and
Kouylekov, Milen and
Negri, Matteo | 2,008 | nan | 173--176 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 866e10618d9e05595dc685a73e1a8965d3aaa391 | 0 |
Definition, Dictionaries and Tagger for Extended Named Entity Hierarchy | Sekine, Satoshi and
Nobata, Chikashi | 2,004 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | b2434644b7178a01f97235a75bddd87b614313af | 1 |
Benchmarking Ontology Tools. A Case Study for the {W}eb{ODE} Platform. | Corcho, Oscar and
Garc{\'\i}a-Castro, Ra{\'u}l and
G{\'o}mez-P{\'e}rez, Asunci{\'o}n | 2,004 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | 4c461cbac24e23e1160ca153bd604dc4fad75285 | 0 |
Extended Named Entity Hierarchy | Sekine, Satoshi and
Sudo, Kiyoshi and
Nobata, Chikashi | 2,002 | nan | nan | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | f664c4a6aee50411f1db79999fd5e7c88a35b926 | 1 |
Handling Noisy Training and Testing Data | Blaheta, Don | 2,002 | nan | 111--116 | 566bd3f672357b8e35343ab6bda4cc25a3071922 | Fine-Grained Entity Typing With a Type Taxonomy: A Systematic Review | c5ecf3a9de15699b86456e64ae4d3dea5c83934a | 0 |
Improving Semantic Parsing via Answer Type Inference | Yavuz, Semih and
Gur, Izzeddin and
Su, Yu and
Srivatsa, Mudhakar and
Yan, Xifeng | 2,016 | nan | 149--159 | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | f3594f9d60c98cac88f9033c69c2b666713ed6d6 | 1 |
Verbal fields in {H}ungarian simple sentences and infinitival clausal complements | Balogh, Kata | 2,016 | nan | 58--66 | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | a42e3bcd05df952558c7d4bac258a02191c83b0d | 0 |
A Rule-based Question Answering System for Reading Comprehension Tests | Riloff, Ellen and
Thelen, Michael | 2,000 | nan | nan | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | 445406b0d88ae965fa587cf5c167374ff1bbc09a | 1 |
Dialogue Helpsystem based on Flexible Matching of User Query with Natural Language Knowledge Base | Kurohashi, Sadao and
Higasa, Wataru | 2,000 | nan | 141--149 | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | 23a99a851485b3d6419e2d98de9ea4e9ea1a34d8 | 0 |
The {TREC}-8 Question Answering Track | Voorhees, Ellen M. and
Tice, Dawn M. | 2,000 | nan | nan | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | 74e03acd5532fbad4c770e9293d2a788b11364f7 | 1 |
Thistle and Interarbora | Calder, Jo | 2,000 | nan | nan | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | 8548b03340130f0e5d8a7880d1f78fa192518e75 | 0 |
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base | Shen, Tao and
Geng, Xiubo and
Qin, Tao and
Guo, Daya and
Tang, Duyu and
Duan, Nan and
Long, Guodong and
Jiang, Daxin | 2,019 | We consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural semantic parsing based approaches usually decompose the task into several subtasks and then solve them sequentially, which leads to following is... | 2442--2451 | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | 788d28e234fc69fb07b4a4da7fb1bcf05e5160b5 | 1 |
Sentence-Level Agreement for Neural Machine Translation | Yang, Mingming and
Wang, Rui and
Chen, Kehai and
Utiyama, Masao and
Sumita, Eiichiro and
Zhang, Min and
Zhao, Tiejun | 2,019 | The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references. In NMT, there is a natural correspondence between the source sentence and the target sentence. However, this relationship has only been represented using the enti... | 3076--3082 | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | dfac457f4f688e9759a6e12acf96ef4b20e18c3d | 0 |
Question Classification using Head Words and their Hypernyms | Huang, Zhiheng and
Thint, Marcus and
Qin, Zengchang | 2,008 | nan | 927--936 | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | 94a9af119df61f501980cf095700f35c2a7762a3 | 1 |
15 Years of Language Resource Creation and Sharing: a Progress Report on {LDC} Activities | Cieri, Christopher and
Liberman, Mark | 2,008 | This paper, the fifth in a series of biennial progress reports, reviews the activities of the Linguistic Data Consortium with particular emphasis on general trends in the language resource landscape and on changes that distinguish the two years since LDCs last report at LREC from the preceding 8 years. After providing... | nan | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | 754580728c0166755db0d6c6f91db2f6a9a53ed7 | 0 |
Performance Issues and Error Analysis in an Open-Domain Question Answering System | Moldovan, Dan and
Pasca, Marius and
Harabagiu, Sanda and
Surdeanu, Mihai | 2,002 | nan | 33--40 | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | 9d0776666d8c7da0f6c40950563687f8ba5b6f7f | 1 |
Getting the message in: a global company{'}s experience with the new generation of low-cost,high-performance machine translation systems | Morland, Vernon | 2,002 | Most large companies are very good at {``}getting the message out{''} {--}publishing reams of announcements and documentation to their employees and customers. More challenging by far is {``}getting the message in{''} {--} ensuring that these messages are read, understood, and acted upon by the recipients. This paper d... | 195--206 | 82bf873a702e005c9e6e2f83d7c4af3fb649e743 | Extreme Classification for Answer Type Prediction in Question Answering | 56271b943f90914fb1bbed737748589efa4b655a | 0 |
Improving Semantic Parsing via Answer Type Inference | Yavuz, Semih and
Gur, Izzeddin and
Su, Yu and
Srivatsa, Mudhakar and
Yan, Xifeng | 2,016 | nan | 149--159 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | f3594f9d60c98cac88f9033c69c2b666713ed6d6 | 1 |
{VR}ep at {S}em{E}val-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity | Henry, Sam and
Sands, Allison | 2,016 | nan | 577--583 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | ca27c3503740b30224115c054bace15bf3e88ab1 | 0 |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing | Xiong, Wenhan and
Wu, Jiawei and
Lei, Deren and
Yu, Mo and
Chang, Shiyu and
Guo, Xiaoxiao and
Wang, William Yang | 2,019 | Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance. Such techniques, however, are not directly applicable to more open and practical scenarios where the type set is not restricted by KB schema and ... | 773--784 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | a0713d945b2e5c2bdeeba68399c8ac6ea84e0ca6 | 1 |
{CASA}-{NLU}: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots | Gupta, Arshit and
Zhang, Peng and
Lalwani, Garima and
Diab, Mona | 2,019 | Natural Language Understanding (NLU) is a core component of dialog systems. It typically involves two tasks - Intent Classification (IC) and Slot Labeling (SL), which are then followed by a dialogue management (DM) component. Such NLU systems cater to utterances in isolation, thus pushing the problem of context managem... | 1285--1290 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 4a0a5f2ac98e8b1ed453265d96f777d2ebc7b679 | 0 |
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing | Chen, Yi and
Cheng, Jiayang and
Jiang, Haiyun and
Liu, Lemao and
Zhang, Haisong and
Shi, Shuming and
Xu, Ruifeng | 2,022 | In this paper, we firstly empirically find that existing models struggle to handle hard mentions due to their insufficient contexts, which consequently limits their overall typing performance. To this end, we propose to exploit sibling mentions for enhancing the mention representations. Specifically, we present two dif... | 2076--2087 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 5a09cd029ffa71cac553405c7fbe927a8ebe9fe7 | 1 |
Delivering Fairness in Human Resources {AI}: Mutual Information to the Rescue | Hemamou, Leo and
Coleman, William | 2,022 | Automatic language processing is used frequently in the Human Resources (HR) sector for automated candidate sourcing and evaluation of resumes. These models often use pre-trained language models where it is difficult to know if possible biases exist. Recently, Mutual Information (MI) methods have demonstrated notable p... | 867--882 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | cdec75f901a93c75ee5386a98abbe44746286e80 | 0 |
Prompt-learning for Fine-grained Entity Typing | Ding, Ning and
Chen, Yulin and
Han, Xu and
Xu, Guangwei and
Wang, Xiaobin and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan and
Li, Juanzi and
Kim, Hong-Gee | 2,022 | As an effective approach to adapting pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using cloze-style language prompts to stimulate the versatile knowledge of PLMs, prompt-learning can achieve promising results on a series of NLP tasks, ... | 6888--6901 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | bf722dc893ddaad5045fca5646212ec3badf3c5a | 1 |
{DPTDR}: Deep Prompt Tuning for Dense Passage Retrieval | Tang, Zhengyang and
Wang, Benyou and
Yao, Ting | 2,022 | Deep prompt tuning (DPT) has gained great success in most natural language processing (NLP) tasks. However, it is not well-investigated in dense retrieval where fine-tuning (FT) still dominates. When deploying multiple retrieval tasks using the same backbone model (e.g., RoBERTa), FT-based methods are unfriendly in ter... | 1193--1202 | 1e87aefc92004a0e4000bb0fa2f5351c3644e8e7 | Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field | 94b34ad657bcfc9f1a8ed1ab1c3144aae9980901 | 0 |
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