Articles

We examine the task of separating types from brands in the food domain. Framing the problem as a ranking task, we convert simple textual features extracted from a domain-specific corpus into a ranker without the need of labeled training data. Such method should rank brands (e.g. sprite) higher than types …...

The “one sense per discourse” (OSPD) and “one sense per collocation” (OSPC) hypotheses have been very influential in Word Sense Disambiguation. The goal of this paper is twofold: (i) to explore whether these hypotheses hold for entities, that is, whether several mentions in the same discourse (or the same collocation) …...

Entity Set Expansion (ESE) aims at automatically acquiring instances of a specific target category. Unfortunately, traditional ESE methods usually have the expansion boundary problem and the semantic drift problem. To resolve the above two problems, this paper proposes a probabilistic Co-Bootstrapping method, which can accurately determine the expansion boundary using …...

Event extraction is a popular research topic in natural language processing. Several event extrac- tion tasks have been defined for both the newswire and biomedical domains. In general, different systems have been developed for the two domains, despite the fact that the tasks in both domains share a number of …...

This paper extends existing word segmentation models to take non-linguistic context into ac- count. It improves the token F-score of a top performing segmentation models by 2.5% on a 27k utterances dataset. We posit that word segmentation is easier in-context because the learner is not trying to access irrelevant lexical …...

We propose a novel approach to deciphering short monoalphabetic ciphers that combines both character-level and word-level language models. We formulate decipherment as tree search, and use Monte Carlo Tree Search (MCTS) as a fast alternative to beam search. Our experiments show a significant improvement over the state of the art …...

This work is concerned with distinguishing different semantic relations which exist between distributionally similar words. We compare a novel approach based on training a linear Support Vector Machine on pairs of feature vectors with state-of-the-art methods based on distributional similarity. We show that the new supervised approach does better even …...

In this paper, we explore the relationship between the human-encoded semantics of ontologies and their application to natural language processing (NLP) tasks, such as word-sense disambig- uation (WSD), for which such ontologies may not have been originally designed. We present a method for assessing the semantic content of an ontology …...

This paper proposes a soft dependency matching model for hierarchical phrase-based (HPB) machine translation. When a HPB rule is extracted, we enrich it with dependency knowledge automatically learnt from the training data. The dependency knowledge not only encodes the dependency relations between the components inside the rule, but also contains …...

In this paper we address the question of the reliability of the predictions made by MT Quality Estimation (QE) systems. In particular, we show that standard supervised QE systems, usually trained to minimize MAE, make serious mistakes at predicting the quality of the sentences in the tails of the quality …...

In this paper, we propose an augmented dependency-to-string model to combine the merits of both the head-dependents relations at handling long distance reordering and the fixed and floating structures at handling local reordering. For this purpose, we first compactly represent both the head-dependent relation and the fixed and floating structures …...

This paper presents a knowledge base containing triples involving pairs of verbs associated with semantic or discourse relations. The relations in these triples are marked by discourse connectors between two adjacent instances of the verbs in the triple in the large French corpus, frWaC. We detail several measures that evaluate …...

Certain common lexical attributes such as polarity and formality are continuous, creating chal- lenges for accurate lexicon creation. Here we present a general method for automatically placing words on these spectra, using co-occurrence profiles, counts of co-occurring words within a large corpus, as a feature vector to a supervised ranking …...

Although semi-supervised model can extract the event mentions matching frequent event patterns, it suf- fers much from those event mentions, which match infrequent patterns or have no matching pattern. To solve this issue, this paper introduces various kinds of linguistic knowledge-driven event inference mechanisms to semi-supervised Chinese event extraction. These …...

We propose a transition system for dependency parsing with a left-corner parsing strategy. Unlike parsers with conventional transition systems, such as arc-standard or arc-eager, a parser with our system correctly predicts the processing difficulties people have, such as of center-embedding. We characterize our transition system by comparing its oracle behaviors …...

We propose an algorithm for coreference resolution based on analogy with shift-reduce pars- ing. By reconceptualising the task in this way, we unite ranking- and cluster-based approaches to coreference resolution, which have until now been largely orthogonal. Additionally, our framework naturally lends itself to rich discourse modelling, which we use …...

Distantly supervised relation extraction, which can automatically generate training data by align- ing facts in the existing knowledge bases to text, has gained much attention. Previous work used conjunction features with coarse entity types consisting of only four types to train their model- s. Entity types are important indicators for …...

In corpus linguistics there have been numerous attempts to compile balanced corpora, result- ing in text collections such as the Brown Corpus or the British National Corpus. These cor- pora are meant to reflect the average language use a native speaker typically encounters. But is it possible to measure in …...

Collections of relational paraphrases have been automatically constructed from large text cor- pora, as a WordNet counterpart for the realm of binary predicates and their surface forms. How- ever, these resources fall short in their coverage of hypernymy links (subsumptions) among the synsets of phrases. This paper closes this gap …...

In this work we present an annotation framework to capture causality between events, inspired by TimeML, and a language resource covering both temporal and causal relations. This data set is then used to build an automatic extraction system for causal signals and causal links between given event pairs. The evaluation …...