acl acl2012 acl2012-99 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Remy Kessler ; Xavier Tannier ; Caroline Hagege ; Veronique Moriceau ; Andre Bittar
Abstract: We present an approach for detecting salient (important) dates in texts in order to automatically build event timelines from a search query (e.g. the name of an event or person, etc.). This work was carried out on a corpus of newswire texts in English provided by the Agence France Presse (AFP). In order to extract salient dates that warrant inclusion in an event timeline, we first recognize and normalize temporal expressions in texts and then use a machine-learning approach to extract salient dates that relate to a particular topic. We focused only on extracting the dates and not the events to which they are related.
Reference: text
sentIndex sentText sentNum sentScore
1 mori ceau@ l ims i fr Abstract We present an approach for detecting salient (important) dates in texts in order to automatically build event timelines from a search query (e. [sent-8, score-1.129]
2 In order to extract salient dates that warrant inclusion in an event timeline, we first recognize and normalize temporal expressions in texts and then use a machine-learning approach to extract salient dates that relate to a particular topic. [sent-13, score-1.909]
3 We focused only on extracting the dates and not the events to which they are related. [sent-14, score-0.646]
4 1 Introduction Our aim here was to build thematic timelines for a general domain topic defined by a user query. [sent-15, score-0.182]
5 They use little temporal information, generally only using document metadata, such as the document creation time (DCT). [sent-19, score-0.308]
6 The few systems that do make use of temporal information (such as the now discontinued Google timeline), only extract absolute, full dates (that feature a day, month and year). [sent-20, score-0.8]
7 1, we found that only 7% of extracted temporal expressions are absolute dates. [sent-22, score-0.359]
8 com We distinguish our work from that of previous researchers in that we have focused primarily on extracted temporal information as opposed to other textual content. [sent-25, score-0.289]
9 We show that using linguistic temporal processing helps extract important events in texts. [sent-26, score-0.383]
10 Our system extracts a maximum of temporal information and uses only this information to detect salient dates for the construction of event timelines. [sent-27, score-1.101]
11 Each date is presented with a set of relevant sentences. [sent-30, score-0.232]
12 The system used for temporal analysis is described in Section 4, and the strategy used for indexing and finding salient dates, as well as the results obtained, are given in Section 51. [sent-34, score-0.444]
13 , 2010) is a specification language for manual annotation of temporal information in texts, but, to the best of our knowledge, it has not yet actually been used in information retrieval systems. [sent-36, score-0.283]
14 , 2009), among others, have highlighted that the analysis of temporal information is often an essential component in text understanding and is useful in a wide range of information retrieval applications. [sent-43, score-0.283]
15 , 2009) highlight the importance of processing temporal expressions in Question Answering systems. [sent-45, score-0.293]
16 For example, in the TREC-10 QA evaluation campaign, more than 10% of questions required an element of temporal processing in order to be correctly processed (Li et al. [sent-46, score-0.236]
17 In multidocument summarization, temporal processing enables a system to detect redundant excerpts from various texts on the same topic and to present results in a relevant chronological order (Barzilay and Elhadad, 2002). [sent-48, score-0.356]
18 (Kim and Choi, 2011) present work on the extraction of temporal information in clinical narrative texts. [sent-50, score-0.282]
19 , 2011) present an end-to-end system that processes clinical records, detects events and constructs timelines of patients’ medical histories. [sent-52, score-0.305]
20 His method used place/time collocations and ranked events according to statistical measures. [sent-59, score-0.167]
21 (Chieu and Lee, 2004) propose a similar system that extracts events relevant to a query from a collection of documents. [sent-63, score-0.2]
22 Important events are those reported in a large number of news articles and each event is constructed according to one single query and represented by a set of sentences. [sent-64, score-0.416]
23 (Swan and Allen, 2000) present an approach to generating graphical timelines that involves extracting clusters of noun phrases and named entities. [sent-65, score-0.182]
24 Each document is an XML file containing a title, a date of creation (DCT), set of keywords, and textual content split into paragraphs. [sent-73, score-0.311]
25 2 AFP Chronologies AFP “chronologies” (textual event timelines) are a specific type of articles written by AFP journalists in order to contextualize current events. [sent-75, score-0.234]
26 These chronologies may concern any topic discussed in the media, and consist in a list of dates (typically between 10 and 20) associated with a text describing the related event(s). [sent-76, score-0.84]
27 We selected 91 chronologies satisfying the following constraints: • All dates in the chronologies are between 2004 aAnldl 2a0te1s s1 i nto th eb e c sure tohgaite sth aere r beelatwteede nev 2e0n0t4s are described in the corpus. [sent-79, score-1.082]
28 For example, “Chronology of climax to Vietnam War” was excluded because its corresponding dates do not appear in the content of the articles. [sent-80, score-0.536]
29 • • All dates in the chronology are anterior to the chronology’s hcere cahtiroonn dlaogtey. [sent-81, score-0.656]
30 The temporal granularity of the chronology is tThhee day. [sent-83, score-0.389]
31 For learning and evaluation purposes, all chronologies were converted to a single XML format. [sent-85, score-0.273]
32 Each document was manually associated with a user search query made up of the keywords required to retrieve the chronology. [sent-86, score-0.166]
33 First, pre-processing of the AFP corpus tags and normalizes temporal expressions in each of the articles (step in the Figure). [sent-89, score-0.293]
34 These documents can be filtered ({), and dates are extracted from the remaining documents. [sent-92, score-0.536]
35 These dates are then ranked in order to show the most important ones to the user (|), to- x 2http : / / lucene . [sent-93, score-0.737]
36 4 Temporal and Linguistic Processing In this section, we describe the linguistic and temporal information extracted during the pre-processing phase and how the extraction is carried out. [sent-97, score-0.273]
37 It also performs named entity recognition (NER) of the most usual named entity categories and recognizes temporal expressions. [sent-104, score-0.302]
38 In the following subsections, we give details of the linguistic information that is used for the detec- tion of salient dates. [sent-109, score-0.217]
39 3 Temporal Analysis A previous module for temporal analysis was developed and integrated into the English grammar (Hag e`ge and Tannier, 2008), and evaluated during TempEval campaign (Verhagen et al. [sent-116, score-0.264]
40 Our goal with temporal analysis is to be able to tag and normalize3 aselected subsetoftemporalexpressions (TEs) which we consider to be relevant for our task. [sent-119, score-0.276]
41 1 Absolute Dates Absolute dates are dates that can be normalized without external or contextual knowledge. [sent-123, score-1.108]
42 3We call normalization the operation of turning a temporal expression into a formated, fully specified representation. [sent-126, score-0.265]
43 733 However, absolute dates are relatively infrequent in our corpus (7%), so in order to broaden the coverage for the detection of salient dates, we decided to consider relative dates, which are far more frequent. [sent-128, score-0.82]
44 2 DCT-relative Dates DCT-relative temporal expressions are those which are relative to the creation date of the document. [sent-131, score-0.485]
45 This class represents 40% of dates extracted from the AFP corpus. [sent-132, score-0.536]
46 External information is required, in particular, the date which corresponds to the moment of utterance. [sent-134, score-0.192]
47 This is the case of expressions like next Friday, which correspond to the calendar date of the first Friday following the DCT. [sent-138, score-0.283]
48 However, these underspecified dates are not used in our experiments. [sent-150, score-0.536]
49 4 Modality and Reported Speech An important issue that can affect the calculation of salient dates is the modality associated with timestamped events in text. [sent-152, score-0.974]
50 For instance, the status of a salient date candidate in a sentence like “The meeting takes place on Friday” has to be distinguished from the one in “The meeting should take place on Friday” or “The meeting will take place on Friday, Mr. [sent-153, score-0.462]
51 The time-stamped event meeting takes place is factual in the first example and can be taken as granted. [sent-155, score-0.179]
52 This is expressed by the modality introduced by the modal auxiliary should (second example), or by the use of the future tense or reported speech (third example). [sent-157, score-0.284]
53 More specifically, we consider the following features: Events that are mentioned in the future: If a time-stamped event is in the future tense, we add a specific attribute MODALITY with value FUTURE to the corresponding TE annotation. [sent-159, score-0.189]
54 Events used with a modal verb: If a timestamped event is introduced by a modal verb such as should or would, then attribute MODALITY to the corresponding TE annotation has the value MODAL. [sent-160, score-0.381]
55 We dealt with time-stamped events governed by a reported speech verb, or otherwise appearing in reported speech. [sent-162, score-0.267]
56 If a relevant TE modifies a reported speech verb, the annotation of this TE contains a specific attribute, DECLARATION=”YES”. [sent-164, score-0.169]
57 If the relevant TE modifies a verb that appears in a clause introduced by a reported speech verb then the annotation contains the attribute REPORTED=”YES”. [sent-165, score-0.305]
58 modality and reported speech can occur for a same time-stamped event). [sent-168, score-0.186]
59 Hong said” is annotated with both modality and reported speech attributes. [sent-170, score-0.186]
60 5 Corpus-dependent Special Cases While we developed the linguistic and temporal annotators, we took into account some specificities of our corpus. [sent-172, score-0.273]
61 We decided that the TEs today and 734 now were not relevant for the detection of salient dates. [sent-173, score-0.258]
62 In the AFP news corpus, these expressions are mostly generic expressions synomymous with nowadays and do not really time-stamp an event with respect to the DCT. [sent-174, score-0.312]
63 Another specificity of the corpus is the fact that if the DCT corresponds to a Monday, and if an event in a past tense is described with the associated TE on Monday or Monday, it means that this event occurs on the DCT day itself, and not on the Monday before. [sent-175, score-0.41]
64 The annotation of the relevant TE has the attribute indicating that it time-stamps an event realized by a reported speech verb. [sent-186, score-0.328]
65 5 millions temporal expressions were detected, among which 845,000 absolute dates (7%) and 4. [sent-190, score-0.923]
66 Although we have not yet evaluated our tagging of relative dates, the system on which our current date normalization is based achieved good results in the TempEval (Verhagen et al. [sent-192, score-0.221]
67 2, we present our experiments using simple filtering and statistics on dates calculated by Lucene. [sent-198, score-0.594]
68 luc(d) ins tshe t sum eofr Lucene scores for textual units containing the date d. [sent-203, score-0.275]
69 logdfN(d) where f(d) is the number of occurrences of date d in the sentence (generally, f(d) = 1), N is the number of indexed sentences and df(d) is the number of sentences containing date d. [sent-207, score-0.443]
70 In all experiments (including baselines), timelines have been built by considering only dates between the first and the last dates of the corresponding manual chronology. [sent-208, score-1.221]
71 Processing runs were evaluated on manually-written chronologies (see Section 3. [sent-209, score-0.364]
72 Note that in this baseline, as well as in all the subsequent runs, the information unit was the sentence because a date was associated to a small part of the text. [sent-236, score-0.223]
73 Same as BLabs, except that sentences con- taining no absolute dates were considered and associated to the DCT. [sent-239, score-0.633]
74 This sentence was indexed with the title and keywords of the AFP article containing it. [sent-255, score-0.153]
75 Combinations between the following filtering operations were possible, by removing all dates associated with a reported speech verb (R), a modal verb (M) and/or a future verb (F). [sent-257, score-0.925]
76 All these filtering operations were intended to remove references to events that were not certain, thereby minimizing noise in results. [sent-258, score-0.168]
77 These processing runs are named SD runs, with indices representing the filtering operations. [sent-259, score-0.182]
78 In all combinations, dates were ranked by the sum of Lucene scores for these sentences (luc) or by tf. [sent-261, score-0.593]
79 3 Machine-Learning Runs We used our set of manually-written chronologies as a training corpus to perform machine learning experiments. [sent-269, score-0.273]
80 We used IcsiBoost5, an implementa4We do not present runs where dates are ranked by the number of times they appear in retrieved sentences (occ), as we did for baselines, since results are systematically lower. [sent-270, score-0.684]
81 In our approach, we consider two classes: salient dates are dates that have an entry in the manual chronologies, while non-salient dates are all other dates. [sent-274, score-1.788]
82 The choices of journalists are indeed very subjective, and chronologies must not exceed a certain length, which means that relevant dates can be thrown away. [sent-276, score-0.934]
83 We rather aggregated all sentences corresponding to the same date before learning the classifier. [sent-281, score-0.192]
84 Features representing the fact that an important event is still written about, a long time after it occurs: 1) Distance between the date and the most recent mention ofthis date 2) Distance between the date and the DCT; 3. [sent-285, score-0.725]
85 Instead, we used the predicted probability P(d) returned by the classifier, and mixed it with the Lucene score of sentences, or with date tf. [sent-288, score-0.224]
86 7 g9 0R103u853ns∗ Table 3: MAP results for salient date extraction with machine-learning. [sent-291, score-0.372]
87 Our 91 chronologies were randomly divided into 4 sub-samples, each of them being used once as test data. [sent-302, score-0.273]
88 However, assembling such a chronology is a very subjective task, and no clear method for evaluation agreement between two journalists seems immediately apparent. [sent-308, score-0.205]
89 We asked him to assess the first 30 dates of these runs. [sent-312, score-0.536]
90 ics6 6Namely, “Arab revolt timeline for Morocco ”, “Kyrgyzstan unrest timeline ”, “Lebanon ’s new government: a timeline ”, “Libya timeline ”. [sent-313, score-0.456]
91 9751967815982 Table 4: Average precision results for manual evaluation on 4 topics, against the original chronologies (APC), and the expert assessment (APE). [sent-316, score-0.303]
92 Table 4 presents results for this evaluation, comparing average precision values obtained 1) against the original, manual chronologies (APC), and 2) against the expert assessment (APE). [sent-317, score-0.303]
93 These values show that, for 3 runs out of 4, many dates returned by the system are considered as valid by the expert, even if not presented in the original chronology. [sent-318, score-0.659]
94 In future work, we envisage providing, together with the detection of salient dates, a semantic analysis that will help determine the importance of events. [sent-322, score-0.218]
95 Another interesting direction in which we soon aim to work is to consider all textual excerpts that are associated with salient dates, and use clustering techniques to determine if textual excerpts correspond to the same event or not. [sent-323, score-0.556]
96 Finally, as our news corpus is available both for English and French (comparable corpus, not necessarily translations), we aim to investigate cross-lingual extraction of salient dates and salient events. [sent-324, score-0.945]
97 Building timelines from narrative clinical records: initial results based-on deep natural language understanding. [sent-382, score-0.195]
98 Recognizing temporal information in korean clinical narratives through text normalization. [sent-391, score-0.282]
99 Text classification and named entities for new event detection. [sent-395, score-0.182]
100 Detecting events with date and place information in unstructured text. [sent-429, score-0.332]
wordName wordTfidf (topN-words)
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