Articles

This paper describes a novel probabilistic ap- proach for generating natural language sen- tences from their underlying semantics in the form of typed lambda calculus. The approach is built on top of a novel reduction-based weighted synchronous context free grammar formalism, which facilitates the transforma- tion process from typed lambda …...

Due to its explicit modeling of the grammaticality of the output via target-side syntax, the string-to-tree model has been shown to be one of the most successful syntax-based translation models. However, a major limitation of this model is that it does not utilize any useful syntactic information on the source …...

Attempts to profile authors according to their characteristics extracted from textual data, in- cluding native language, have drawn attention in recent years, via various machine learn- ing approaches utilising mostly lexical fea- tures. Drawing on the idea of contrastive analysis, which postulates that syntactic er- rors in a text are …...

A rumor is commonly defined as a state- ment whose true value is unverifiable. Ru- mors may spread misinformation (false infor- mation) or disinformation (deliberately false information) on a network of people. Identi- fying rumors is crucial in online social media where large amounts of information are easily spread across …...

It is popular for users in Web 2.0 era to freely annotate online resources with tags. To ease the annotation process, it has been great interest in automatic tag suggestion. We propose a method to suggest tags according to the text description of a resource. By consid- ering both the …...

With the recent rise in popularity and scale of social media, a growing need exists for systems that can extract useful information from huge amounts of data. We address the issue of detecting influenza epidemics. First, the proposed system extracts influen- za related tweets using Twitter API. Then, only tweets …...

We present a named entity recognition (NER) system for extracting product attributes and values from listing titles. Information extrac- tion from short listing titles present a unique challenge, with the lack of informative con- text and grammatical structure. In this work, we combine supervised NER with bootstrap- ping to expand …...

Supervised classification needs large amounts of annotated training data that is expensive to create. Two approaches that reduce the cost of annotation are active learning and crowd- sourcing. However, these two approaches have not been combined successfully to date. We evaluate the utility of active learning in crowdsourcing on two …...

Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary. This paper shows that the output of state-of- the-art Open IE systems is rife with uninfor- mative and incoherent extractions. To over- come these problems, we introduce two sim- ple syntactic and …...

Linear models have enjoyed great success in structured prediction in NLP. While a lot of progress has been made on efficient train- ing with several loss functions, the problem of endowing learners with a mechanism for feature selection is still unsolved. Common approaches employ ad hoc filtering or L1- regularization; …...

People tweet more than 100 Million times daily, yielding a noisy, informal, but some- times informative corpus of 140-character messages that mirrors the zeitgeist in an un- precedented manner. The performance of standard NLP tools is severely degraded on tweets. This paper addresses this issue by re-building the NLP pipeline …...

We consider the problem of learning fac- tored probabilistic CCG grammars for seman- tic parsing from data containing sentences paired with logical-form meaning representa- tions. Traditional CCG lexicons list lexical items that pair words and phrases with syntac- tic and semantic content. Such lexicons can be inefficient when words appear …...

We present an online learning algorithm for training parsers which allows for the inclusion of multiple objective functions. The primary example is the extension of a standard su- pervised parsing objective function with addi- tional loss-functions, either based on intrinsic parsing quality or task-specific extrinsic mea- sures of quality. Our …...

We propose a novel forest reranking algorithm for discriminative dependency parsing based on a variant of Eisner’s generative model. In our framework, we define two kinds of gener- ative model for reranking. One is learned from training data offline and the other from a for- est generated by a baseline …...

We explore unsupervised approaches to rela- tion extraction between two named entities; for instance, the semantic bornIn relation be- tween a person and location entity. Con- cretely, we propose a series of generative probabilistic models, broadly similar to topic models, each which generates a corpus of ob- served triples of …...

Traditional approaches to Relation Extraction from text require manually defining the rela- tions to be extracted. We propose here an ap- proach to automatically discovering relevant relations, given a large text corpus plus an ini- tial ontology defining hundreds of noun cate- gories (e.g., Athlete, Musician, Instrument). Our approach discovers …...

It is often assumed that ‘grounded’ learning tasks are beyond the scope of grammatical in- ference techniques. In this paper, we show that the grounded task of learning a seman- tic parser from ambiguous training data as dis- cussed in Kim and Mooney (2010) can be re- duced to a …...

This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from previously trained relation detec- tors. Specifically, we detect a new semantic relation by projecting the new relation’s train- ing instances …...

We report on empirical results in extreme extraction. It is extreme in that (1) from re- ceipt of the ontology specifying the target concepts and relations, development is li- mited to one week and that (2) relatively little training data is assumed. We are able to surpass human recall and …...

Context-dependent word similarity can be measured over multiple cross-cutting dimen- sions. For example, lung and breath are sim- ilar thematically, while authoritative and su- perficial occur in similar syntactic contexts, but share little semantic similarity. Both of these notions of similarity play a role in deter- mining word meaning, and …...