Articles

Question Answering over Linked Data (QALD) aims to evaluate a question an- swering system over structured data, the key objective of which is to translate questions posed using natural language into structured queries. This technique can help common users to directly ac- cess open-structured knowledge on the Web and, accordingly, …...

Knowledge graphs are recently used for enriching query representations in an entity-aware way for the rich facts or- ganized around entities in it. How- ever, few of the methods pay attention to non-entity words and clicked websites in queries, which also help conveying user intent. In this paper, we tackle …...

In this paper we propose a computational method for determining the orthographic similarity between Romanian and related languages. We account for etymons and cognates and we investigate not only the number of related words, but also their forms, quantifying orthographic similari- ties. The method we propose is adaptable to any …...

Compositional distributional semantics is a subfield of Computational Linguistics which investigates methods for represent- ing the meanings of phrases and sen- tences. In this paper, we explore im- plementations of a framework based on Combinatory Categorial Grammar (CCG), in which words with certain grammatical types have meanings represented by multi- …...

Most word representation methods assume that each word owns a single semantic vec- tor. This is usually problematic because lexical ambiguity is ubiquitous, which is also the problem to be resolved by word sense disambiguation. In this paper, we present a unified model for joint word sense representation and disambiguation, …...

Dependency parsing with high-order fea- tures results in a provably hard decoding problem. A lot of work has gone into developing powerful optimization meth- ods for solving these combinatorial prob- lems. In contrast, we explore, analyze, and demonstrate that a substantially simpler randomized greedy inference algorithm al- ready suffices for …...

The role of Web search queries has been demonstrated in the extraction of attributes of instances and classes, or of sets of re- lated instances and their class labels. This paper explores the acquisition of open- domain commonsense knowledge, usu- ally available as factual knowledge, from Web search queries. Similarly …...

We describe a new dependency parser for English tweets, TWEEBOPARSER. The parser builds on several contributions: new syntactic annotations for a corpus of tweets (TWEEBANK), with conventions informed by the domain; adaptations to a statistical parsing algorithm; and a new approach to exploiting out-of-domain Penn Treebank data. Our experiments show …...

We introduce a new CCG parsing model which is factored on lexical category as- signments. Parsing is then simply a de- terministic search for the most probable category sequence that supports a CCG derivation. The parser is extremely simple, with a tiny feature set, no POS tagger, and no statistical …...

We investigate grammatical error detec- tion in spoken language, and present a data-driven method to train a dependency parser to automatically identify and label grammatical errors. This method is ag- nostic to the label set used, and the only manual annotations needed for training are grammatical error labels. We find …...

There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale. Nearly all this work, however, assumes a sin- gle vector per word type—ignoring poly- semy and thus jeopardizing their useful- ness for downstream tasks. We …...

Code-mixing is frequently observed in user generated content on social media, especially from multilingual users. The linguistic complexity of such content is compounded by presence of spelling vari- ations, transliteration and non-adherance to formal grammar. We describe our initial efforts to create a multi-level an- notated corpus of Hindi-English code- …...

In this paper we propose a method to increase dependency parser performance without using additional labeled or unla- beled data by refining the layer of pre- dicted part-of-speech (POS) tags. We per- form experiments on English and Ger- man and show significant improvements for both languages. The refinement is based …...

Importance weighting is a generalization of various statistical bias correction tech- niques. While our labeled data in NLP is heavily biased, importance weighting has seen only few applications in NLP, most of them relying on a small amount of labeled target data. The publication bias toward reporting positive results makes …...

Different approaches to high-quality grammatical error correction have been proposed recently, many of which have their own strengths and weaknesses. Most of these approaches are based on classi- fication or statistical machine translation (SMT). In this paper, we propose to com- bine the output from a classification-based system and an …...

The syntactic ambiguity of a transitive verb (Vt) followed by a noun (N) has long been a problem in Chinese parsing. In this paper, we propose a classifier to resolve the ambiguity of Vt-N structures. The design of the classifier is based on three important guidelines, namely, adopting linguistically motivated …...

In this paper we address the problem of multilingual part-of-speech tagging for resource-poor languages. We use par- allel data to transfer part-of-speech in- formation from resource-rich to resource- poor languages. Additionally, we use a small amount of annotated data to learn to “correct” errors from projected approach such as tagset …...

We explore the impact of morpholog- ical segmentation on keyword spotting (KWS). Despite potential benefits, state- of-the-art KWS systems do not use mor- phological information. In this paper, we augment a state-of-the-art KWS sys- tem with sub-word units derived from su- pervised and unsupervised morphological segmentations, and compare with phonetic …...

In this paper, we propose novel struc- tured language modeling methods for code mixing speech recognition by incorporat- ing a well-known syntactic constraint for switching code, namely the Functional Head Constraint (FHC). Code mixing data is not abundantly available for training language models. Our proposed meth- ods successfully alleviate this …...

Most studies on statistical Korean word spac- ing do not utilize the information provided by the input sentence and assume that it was completely concatenated. This makes the word spacer ignore the correct spaced parts of the input sentence and erroneously alter them. To overcome such limit, this paper proposes …...