Although distributional models of word meaning have been widely used in Information Retrieval achieving an effective representation and generalization schema of words in isolation, the composition of words in phrases or sentences is still a challenging task. Different methods have been proposed to account on syntactic structures to combine words in term of algebraic operators (e.g. tensor produ...

Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization where the goal is to produce a single text as a compressed version of a set of documents with a minimum loss of relevant information. In this paper, we experiment with one empirical method and two unsupervised statistic...

We describe the system from the Natural Language Processing group at Microsoft Research for the BioNLP 2011 Shared Task. The task focuses on event extraction, identifying structured and potentially nested events from unannotated text. Our approach follows a pipeline, first decorating text with syntactic information, then identifying the trigger words of complex events, and finally identifying t...

Empirical distributional methods account for the meaning of syntactic structures by combining words according to algebraic operators (e.g. tensor product) acting over the corresponding lexical constituents. In this paper, a novel approach for semantic composition based on space projection techniques over the basic geometric lexical representations is proposed. In line with Frege’s context princ...

At the beginning of 2021, official use Civil Code People's Republic China (hereinafter referred to as "the Code") marked a major achievement in construction our country's socialist legal system. Different texts often have different functions, structures and language characteristics. This article takes English translation Personality Rights an example. Based on original text Personal Code, this ...

Most work on automatic text simplification considers lexical difficulty separate from syntactic simplification. In this study, we use both factors together to predict a variety of sentence changes, including the standard problems of splitting and shortening, as well as expanding to define difficult words that are important to the topic. We leverage a variety of lexical and parse features, as we...

First language (L1) interactants quickly develop a coordinated form of communication, reusing each other's linguistic choices and aligning to their partner (Pickering & Garrod, 2021). More recently, research became interested in second (L2) alignment (cf., Kim Michel, this issue). Earlier work has shown that both lexical syntactic can be found L2 dialogue, with task type context as potential me...

We present the implementation of a system which extracts not only lexicalized grammars but also feature-based lexicalized grammars from Korean Sejong Treebank. We report on some practical experiments where we extract TAG grammars and tree schemata. Above all, full-scale syntactic tags and well-formed morphological analysis in Sejong Treebank allow us to extract syntactic features. In addition, ...

We present an experimental study making use of a machine learning approach to identify the factors that affect the aspectual value that characterises polysemous verbs under each of their readings. The study is based on various morpho-syntactic and semantic features collected from a French lexical resource and on a gold standard aspectual classification of verb readings designed by an expert. Ou...