Temporal information retrieval has been a topic of great interest in recent years. Its purpose is to improve the effectiveness of information retrieval methods by exploiting temporal information in documents and queries. In this article, we present a survey of the existing literature on temporal information retrieval. In addition to giving an overview of the field, we categorize the relevant research, describe the main contributions, and compare different approaches. We organize existing research to provide a coherent view, discuss several open issues, and point out some possible future research directions in this area. Despite significant advances, the area lacks a systematic arrangement of prior efforts and an overview of state-of-the-art approaches. Moreover, an effective end-to-end temporal retrieval system that exploits temporal information to improve the quality of the presented results remain undeveloped.

The availability of contiguous and non-contiguous multiword lexical units (MWUs) in Natural Language Processing (NLP) lexica enhances parsing precision, helps attachment decisions, improves indexing in information retrieval (IR) systems, reinforces information extraction (IE) and text mining, among other applications. Unfortunately, their acquisition has long been a significant problem in NLP, IR and IE. In this paper we propose two new association measures, the Symmetric Conditional Probability (SCP) and the Mutual Expectation (ME) for the extraction of contiguous and non-contiguous MWUs. Both measures are used by a new algorithm, the LocalMaxs, that requires neither empirically obtained thresholds nor complex linguistic filters. We assess the results obtained by both measures by comparing them with reference association measures (Specific Mutual Information, φ 2 , Dice and Log-Likelihood coefficients) over a multilingual parallel corpus. An additional experiment has been carried out over a part-of-speech tagged Portuguese corpus for extracting contiguous compound verbs. 10 This corpus corresponds to the news of some days in January 1994 from Lusa (the Portuguese News Agency). 11 Note the spelling error in 'Republica' that should have been written as 'República'. However real corpus is like that and we can not escape from it as there are texts that may reproduce parts of other texts where the graphical form of words does not correspond to currently accepted way of writing. 12 We have discarded hapaxes, every "MWU" or "relevant expression" that occurred just once.

This paper describes an original hybrid system that extracts multiword unit candidates from part-of-speech tagged corpora. While classical hybrid systems manually define local part-ofspeech patterns that lead to the identification of well-known multiword units (mainly compound nouns), our solution automatically identifies relevant syntactical patterns from the corpus. Word statistics are then combined with the endogenously acquired linguistic information in order to extract the most relevant sequences of words. As a result, (1) human intervention is avoided providing total flexibility of use of the system and (2) different multiword units like phrasal verbs, adverbial locutions and prepositional locutions may be identified. The system has been tested on the Brown Corpus leading to encouraging results.

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