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This paper describes the two algorithms we developed for the CoNLL 2008 Shared Task “Joint learning of syntactic and se- mantic dependencies”. Both algorithms start parsing the sentence using the …...

This paper describes a system to solve the joint learning of syntactic and seman- tic dependencies. An directed graphical model is put forward to integrate depen- dency relation classification and …...

We present a comparison between two systems for establishing syntactic and semantic depen- dencies: one that performs dependency parsing and semantic role labeling as a single task, and another that …...

State-of-the-art systems for grammatical er- ror correction are based on a collection of independently-trained models for specific er- rors. Such models ignore linguistic interac- tions at the sentence level and …...

The creation of a pronunciation lexicon re- mains the most inefficient process in develop- ing an Automatic Speech Recognizer (ASR). In this paper, we propose an unsupervised alternative – requiring …...

We address the problem of automatically inferring the tense of events in Chinese text. We use a new corpus annotated with Chinese semantic tense information and other implicit Chinese linguistic …...

This paper proposes a history-based struc- tured learning approach that jointly ex- tracts entities and relations in a sentence. We introduce a novel simple and flexible table representation of entities …...

We present a method to jointly learn fea- tures and weights directly from distri- butional data in a log-linear framework. Specifically, we propose a non-parametric Bayesian model for learning phonologi- …...

Categories such as ANIMAL or FURNITURE are acquired at an early age and play an impor- tant role in processing, organizing, and con- veying world knowledge. Theories of cat- egorization …...

We present a novel method for jointly learning compositional and non- compositional phrase embeddings by adaptively weighting both types of em- beddings using a compositionality scoring function. The scoring function …...

AbstractNatural language understanding and dialog management are two integral components of interactive dialog systems. Previous research has used machine learning techniques to individually optimize these components, with different forms of …...

AbstractPre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level …...

AbstractWe propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using …...

AbstractRecent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that …...

AbstractBilingual word embeddings have been widely used to capture the correspondence of lexical semantics in different human languages. However, the cross-lingual correspondence between sentences and words is less studied, despite …...

AbstractWe propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global latent topics are …...

AbstractChinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks. Previous …...

AbstractWe address a challenging and practical task of labeling questions in speech in real time during telephone calls to emergency medical services in English, which embeds within a broader decision …...

AbstractExtracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying overlapping relations with shared entities. Prior …...

AbstractNamed entity recognition and relation extraction are two important fundamental problems. Joint learning algorithms have been proposed to solve both tasks simultaneously, and many of them cast the joint task …...

AbstractCode-mixing (CM) is a frequently observed phenomenon that uses multiple languages in an utterance or sentence. There are no strict grammatical constraints observed in code-mixing, and it consists of non-standard …...

AbstractIn this paper, we present our text augmentation based approach for the Table Statement Support Subtask (Phase A) of SemEval-2021 Task 9. We experiment with different text augmentation techniques such …...

AbstractThe lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling require abundant training data, …...

AbstractWe consider the problem of multi-label classification, where the labels lie on a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not assume that the label-hierarchy …...

AbstractThis paper describes MagicPai’s system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense. This task aims to detect whether the text is humorous and how humorous …...

AbstractSpoken language understanding, usually including intent detection and slot filling, is a core component to build a spoken dialog system. Recent research shows promising results by jointly learning of those …...

AbstractPrevious studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event …...