Sie möchten wissen, was genau die Digitalisierung von Texten beinhaltet? Sie haben sich schon immer gefragt, wie Texte in einem Korpus optimal durchsuchbar gemacht werden? Sie wundern sich, wie Texte mit linguistischen Informationen angereichert werden können? Dann sind Sie in diesem Kurs genau richtig!! Er bietet einen Überblick über die wichtigsten Konzepte und Probleme bei der Digitalisierung und Annotation von geschriebenen Texten. In sechs thematischen Modulen verteilt auf sechs Wochen lernen Sie relevante Technologien und Werkzeuge kennen. Jedes Modul beinhaltet zwei bis drei Videos (10-20 Minuten), ein Quiz oder ein Peer-Assessment sowie kurze Hintergrundtexte und weiterführende Links zu ausgewählten Themen. Für wen ist dieser Kurs interessant: Dieser Kurs richtet sich an Korpuslinguist/-innen, an Geisteswissenschaftler/-innen und Sprachinteressierte, die von einer rein sprachwissenschaftlichen Perspektive ausgehend auch ein paar Schritte in die Welt der Digitalisierung von Texten wagen und die dahinterstehenden Technologien kennenlernen möchten. Für diesen Kurs brauchen Sie keine Programmierkenntnisse. Mit Interesse an der Digitalisierung und Annotation von Texten sind Sie bestens gerüstet für diesen Kurs. Wir freuen uns, mit Ihnen diese digitalen Wege zu beschreiten, die in den Geisteswissenschaften immer wichtiger werden.
6.2 - Sprachübergreifende Alignierung
Loading...
来自 University of Zurich 的课程
Sprachtechnologie in den Digital Humanities
19 个评分
从本节课中
Woche 6 - Herausforderungen der Multilingualen Textanalyse
Im letzten Modul befassen wir uns mit multilingualen bzw. parallelen Korpora. Im ersten Teil geht es um die automatische Sprach-Identifikation in gemischtsprachlichen Korpora, die einen wichtigen Schritt bei der Verarbeitung der Texte darstellt. Im zweiten Teil geht es um die automatische Satz- bzw. Wortalignierung zwischen parallelen Texten in unterschiedlichen Sprachen. Viel Spaß in der letzten Woche!
与讲师见面
Martin VolkProf. Dr.
Institute of Computational Linguistics
Noah BubenhoferDr.
Institute of Computational Linguistics
Simon ClematideDr. phil.
Institute of Computational Linguistics
Hello and welcome to the second part of Module 6:
"Challenges in Multilingual Text Analysis - cross-lingual alignment"
In the first part of this module, we focussed on automatic language identification
and code-switching, i.e. the automatic detection
of a given language in a certain text segment.
The second part of this module deals with the topic of parallel corpora containing translated texts
and how the parts of such a parallel corpus
can be connected and aligned with each other.
That's what we call cross-lingual alignment.
In this module, we will discuss possible alignments
on the level of sentences and words.
Let's first take a look at parallel corpora in general.
Those are texts with translations that are available in two or more languages.
Some examples:
The texts of the European Parliament, called "Europarl" are very well-known,
I will go into detail in a minute,
and the texts of the Canadian Parliament, the "Canadian Hansard" are used as parallel corpora.
The collection of national laws of the European Union or Switzerland are available in three or four languages,
great amounts of film subtitles are available for several languages.
The subtitles of the TED talks have been translated
into several dozen languages.
And also large collections of patents in various languages.
You see, there is a great amount of different text genres
available as parallel corpora.
Let's take a closer look at the Europarl corpus
where all the utterances from the European Parliament have been collected.
That are very large amounts of text data.
In the current slide, we see that for German
there are 47 million words and 2.1 million sentences in this corpus.
Also the other languages of the European Union
which have been represented in the EU for a long time,
have an comparable amount of text collections.
While for the other languages that recently joined the EU
smaller amounts of texts are available.
All those texts have been preprocessed
and are ready for linguistic and computer-linguistic research and analyses.
You can easily use them for your own investigations. An example:
A very short utterance, close to the style of spoken language:
DE: "Vielen Dank, Herr Segni, das will ich gerne tun." EN: "Thank you, Mister Segni, I shall do so gladly."
But you can also find longer utterances:
DE: "Wie Sie sicher aus der Presse und dem Fernsehen wissen,
gab es in Sri Lanka mehrere Bombenexplosionen mit zahlreichen Toten."
An utterance from January 2000,
which can be found as translated version for all those languages
that were already part of the European Union at that time.
If we want to use such parallel corpora alignments can help to make them more useful to us.
Alignments can take place on all levels of the linguistic structure: from paragraph to paragraph, from sentence to sentence and from word to word.
Let's start with the approaches for automatic sentence alignments.
The question is the following:
We have a pair of translated documents, i.e. d1 and d2 in two languages
and the task of automatic sentence alignment
is to find out which sentence s1 in document d1
is a translation of sentence s2 in document d2.
The alignment of multiple sentences in d1 with one sentence in d2 is of course possible,
it is indeed often the case that two short sentences in a German document
might be translation of a long sentence
in another language, English for example.
We assume that the sentences follow the same order in both texts
when applying approaches for automatic sentence alignment.
These alignment approaches are typically based on the comparison of length,
i.e. the number of characters per sentence, the number of letters, punctuation marks, blank spaces, etc.
are summed up and compared to each other.
The algorithm calculates and identifies
the sentences that most likely correspond to each other.
This algorithm also benefits of so-called "anchor words". Those are words that are identical in both languages, i.e. numbers, numerical expressions.
In this case, when we see "2005" in the German sentence
it is very likely that this year appears also in the French text. You need to take into account that there are several different conventions,
some numbers are written in Arabic numerals in some languages
and in other languages they are expressed by using Roman numerals.
Other typical anchor words are proper nouns, especially personal names
like in this example, "Angela Merkel" or geographical expressions.
Please note that toponyms are often translated,
the German word "Matterhorn" doesn't simply correspond to "Matterhorn" in French
but to "Mont Cervin", the French version of "Matterhorn".
It is important to consider word lists in such cases. Additionally, anchor words can also be words that are written in a similar way
for example German "Lokomotive",
"locomotive" in English or "locomotive" in French.
These words can also be used
to compute alignments beyond sentence boundaries.
Here an example, how this could look like in the Text+Berg corpus.
We start with the first sentence pair
and check if the lengths correspond more or less, which is the case in the current example.
We move on to the second sentence and to the third sentence.
And here we see that the German sentence is very long while the French sentence is really short.
Then, we take the subsequent sentence in French
and check whether there is a correspondence due to anchor words.
If yes, the sentence is added to the current alignment
and we move on to the next sentence pairs. At the position of the long German sentence
which is actually a linked chain of two sentences
where we weren't able to identify the sentence boundary correctly
we see that actually two French sentences correspond to this long German sentence.
A program to compute these alignments is "InterText",
a freely available program that you can find online.
You can upload a parallel text into this tool
and the sentence alignments will be computed at the touch of a button.
That's an algorithm implemented in the system "HunAlign".
This "HunAlign" program is available as single, independent program
or integrated in InterText.
I'd recommend you to use "InterText",
it has a very nice graphical user interface that you can see in the current slide.
In yellow, those sentences are being marked
where the system was not able to find 1:1 translations
but 2:2 sentences or indeed also 1:0 translations.
We see in the next slide,
that this Italian sentence doesn't have a correspondent translation in English. These alignments can be manually corrected and adapted
directly in the "InterText" interface
until you reached the quality of alignments that you expected.
In general, the automatic sentence alignment works fairly well
for a large variety of texts
that are reasonably close translations of each other and don't contain too many omissions.
If we are dealing with texts that are very different,
a slightly different approach based on machine translation
should be used for automatic sentence alignment.
The idea is that we have a sentence s1 in the source document
and we translate this sentence into the language of the target document, French for instance,
by using machine translation.
And if the machine-translated sentence
is similar to the sentence in the target documents, these two sentences will be aligned.
We need thus a machine translation system for this language pair
in order to apply such an approach
which is particularly suitable for texts with many omissions, insertions and many OCR errors.
We can say that these are texts that contain a lot of "noise".
An approach where this has been implemented is called "Bleu Align".
It is a freely available system that you can use for such texts.
That's all about the sentence alignments, let's move on to the word alignments.
We have the following question now:
In a pair of translated sentences s1 and s2, we want to known
which word w1 in sentence s1 is a translation
of a word w2 in sentence s2 in the target language.
Again, we might have alignments
between groups of words in one sentence
and single words in another sentence.
And we cannot assume, that these words
are in the same order in both sentences.
Word alignment is inherently a more complex problem
than automatic sentence alignment.
In the following word alignment example
we have a German sentence at the left and a French sentence at the right.
Due to the arrows
we can understand that those word alignments were the arrows aren't straight
imply that the word order is not identical in the two sentences.
And that's something that we need to take into account when dealing with automatic word alignment.
How does this system work?
Automatic word alignment is based
on the co-occurrence of words in the parallel, translated sentences.
The intuition behind that is:
Take all German sentences where for example the word "Haus" (house) occurs.
Then, look at all French translations.
Which word in these parallel French sentences is the most frequent translation to "Haus" (house)?
And we will find out, that it is the word "maison". We can thus conclude that "maison" is a translation,
a translated version of the German word "Haus" (house).
Large, parallel corpora are required
in order to have sufficient statistical material and evidence.
That's how this works intuitively.
In practice, a complex algorithm is used
called "Expectation Maximisation Algorithm" which applies this method for all the words in a sentence at the same time.
Then, the word alignment with the highest probability is computed. There is a freely available program called "GIZA++"
that can be used to compute the word alignments automatically. Then, we obtain word alignment matrices.
These matrices might look like this for example:
Vertically, we have the German sentence:
"Es war die Zeit, in der Portugal und Spanien die Welt unter sich aufgeteilt hatten."
And horizontally, the English expression:
"It was an age in which Portugal and Spain had divided the world between themselves." The automatically computed alignment for the German word "Zeit" is "age".
For the German word "die", no alignment was found.
That's most likely due to the corpus evidence,
and the fact that the undefined article "an" in the expression "an age"
rarely co-occurs with the German article "die" in our corpus.
That's why the alignment system didn't suggest any corresponding translation.
You can see that also words that aren't in the same order can be aligned properly
like the German "aufgeteilt hatten"
that needs to be aligned with the English expression "had divided".
But there are also alignment errors: The German word "aufgeteilt" has also been aligned with "an age".
That's wrong.
This should show you
that automatic word alignment doesn't always work perfectly fine.
Based on statistical alignment approaches
we can, however, achieve useful results.
All this can also work
in a multi-parallel way.
We computed the word alignments for the languages
German, English, Spanish, French and Italian.
I selected the English word "partnership" and as you can see, the corresponding words "Partnerschaft", "colaboración" are found
and also for French and Italian the alignments are correct. But there is also a wrong alignment,
an additional alignment in French
resulting in a redundant alignment.
You can also see that the alignments are computed in a certain direction: The yellow alignment of "accord" in French
is only an alignment for the direction French -> English and not viceversa.
That's something we can use
if we want to clean the word alignments.
Let's summarise:
The alignments are a very important aspect when pre-processing parallel corpora.
Sentence alignments are usually computed by comparing the sentence length or in special cases, using machine translation.
Word alignments are based on the co-occurrence of words in parallel, aligned sentences.
This alignment information is saved in XML
and can be used for further applications and research.
Parallel corpora are fascinating because this additional information from the translations contains a lot of further information about the source language
and thus allows us to disambiguate.
I would like to conclude this module and thank you for your attention.
Thank you very much!
