Version 4.3

This is version 4.3 of HornMorpho, a Python program that performs morphological analysis, generation, segmentation, and grapheme-to-phoneme conversion in various languages of the Horn of Africa. For the new experimental HornMorpho segmenter for Amharic (version 4.5), see below.

Installation

First download one of the wheel files from the dist/ folder:

• If you're working with Amharic only, download HornMorphoA-4.3-py3-none-any.whl.
• If you're working with Afaan Oromoo only, download HornMorphoO-4.5-py3-none-any.whl.
• Otherwise use the (very large) all-inclusive version: HornMorpho-4.3-py3-none-any.whl.

Then, to install from the wheel file, do the following in a Python shell from the folder where the wheel file is.

pip install HornMorpho-4.*-py3-none-any.whl

(This assumes that you have wheel installed.)

Then to use the program, in a Python shell, do

import hm

If you have problems with installation, contact [email protected].

Functions

Morphological analysis

Morphological analysis takes a word and returns zero or more analyses, each consisting of the root, stem, or lemma of the word and a set of grammatical features.

hm.anal(language, word)
Options: raw=False, um=False

hm.anal_file(language, input_file)
Options: output_file=None

• language is a string representing the language, 'amh' for Amharic for example.

• word is a string representation of the word in the standard orthography for the language.
• input_file is a path to the file to be analyzed.

The function hm.anal attempts to analyze the input word morphologically. Words may be ambiguous, in which case there are multiple analyses. By default, for each analysis the function prints out the lemma, a gloss of the lemma if available, and a description of the grammatical features.

>>> hm.anal('amh', "የቤታችን")
word: የቤታችን
POS: noun, lemma: ቤት|bet, gloss: house
 possessor: 1 plur
 other: definite, genitive

To return the 'raw' internal analysis of the word, use the option raw=True. A list of Python dicts is returned, one for each analysis (this is new in versions 4.0), with keys for lemma, gloss, and grammatical features ('gram').

The grammatical features returned are in the form of a feature structure, a kind of dict with grammatical feature names as keys and their values as values. Among the features you may find useful are sb (subject), tm (tense-aspect-modality), ob (object). (A complete description of these features will appear soon.)

>>> hm.anal('amh', "የቤታችን", raw=True)
[{'lemma': 'ቤት|bet', 'root': 'ቤት|bEt', 'gloss': 'house', 'gram': [-acc,cnj=None,+def,-dis,+gen,-itu,-plr,pos=n,poss=[+expl,+p1,-p2,+plr],pp=None,-prp,t=[eng=house],v=None]}]

To output features from the UniMorph project, use the option um=True. The UniMorph features are returned as a string of feature names separated by semicolons. (A description of the relevant UniMorph features will appear soon.) The UniMorph option currently only works for Amharic and Tigrinya.

>>> hm.anal('amh', "የቤታችን", um=True)
[{'lemma': 'ቤት|bet', 'root': 'ቤት|bEt', 'gloss': 'house', 'gram': 'N;GEN;PSS1P'}]

For verbs and nouns derived from verbs, hm.anal also returns a representation of the root of the word.

>>> hm.anal('amh', "ተፈቀደላት", um=True)
[{'lemma': 'ፈቀደ|fǝqqǝdǝ', 'root': '<fqd:A>', 'gloss': 'permit,allow', 'gram': 'V;PFV;PASS;3;SG;MASC;ARGDA3SF'}]

The function hm.anal_file analyzes the words in a file, printing out the analyses for each word. If output_file is specified, the results are written to that file. The options for hm.anal, raw and um, also apply.

Morphological generation

Morphological generation takes a lemma and a set of grammatical features and returns zero or more word forms.

hm.gen(language, lemma)
Options: features=None, um=None

• language is a string abbreviation of a language.

• lemma is a string representation of a lemma.

This function attempts to generate one or more words from the lemma provided. If no grammatical features are specified, a set of default features is used.

>>> hm.gen('amh', "ፈቀደ")
['ፈቀደ|fǝqqǝdǝ']

Features are specified using the options features, with a string representation of a feature structure of the type returned by hm.anal, or um, with a string representation of a set of UniMorph features. The UniMorph option currently only works for Amharic and Tigrinya.

>>> hm.gen('amh', "ፈቀደ", features="[sb=[+p1,+plr],tm=imf,+neg]")
["አንፈቅድም|'anfǝqdɨm"]

>>> hm.gen('amh', "ፈቀደ", um="1;PL;IPFV;NEG")
["አንፈቅድም|'anfǝqdɨm"]

Segmentation

Morphological segmentation takes a word and returns a representation of the sequence of morphemes making up the word.

hm.seg(language, word)
Options: realize=False

hm.seg_file(language, input_file)
Options: output_file=None

• language is a string representing the language, 'amh' for Amharic for example.

• word is a string representation of the word in the standard orthography for the language.
• input_file is a path to the file to be analyzed.

The function hm.seg takes a language abbreviation and a word in conventional orthography and returns a segmentation of the word. Morphemes are separated by hyphens, with an indication of the grammatical features associated with each morpheme following it in parentheses. For Amharic verbs, the stem appears in curly brackets. Within the stem the root and the consonant-vowel template are separated by a plus sign.

>>> hm.seg('amh', "አንፈቅድም")
አንፈቅድም -- v:'an(neg1,sb=1p)-{fqd+1e23}(imprf)-m(neg2)

With the option realize=True, the morphemes appear in Ge`ez orthography.

>>> hm.seg('amh', "አንፈቅድም", realize=True)
[['አን(neg1,sb=1p)-{ፈቅድ}(imprf)-ም(neg2)']]

The function hm.seg_file behaves like hm.anal_file except that it prints out a segmentation of the words in the file instead of a morphological analysis.

Grapheme-to-phoneme conversion

The Ge`ez orthography that is used for Ethio-Eritrean Semitic languages faithfully represents almost all aspects of the phonology of the languages. However, it fails to represent consonant gemination and vowel epenthesis.

Gemination refers to the lengthening of consonants. In most Ethio-Eritrean Semitic languages, it is both a property of particular lexical items (ልብ lɨbb) and a feature of verb morphology. In rare cases, this can result in ambiguity (ይመታል yɨmǝtal 'he hits', yɨmmǝttal 'he is hit'). Gemination is also crucial for speech synthesis.

Epenthesis refers to the process of inserting a vowel to break up sequences of consonants. In the Ge`ez orthography, the sixth order characters represent both bare consonants and consonants followed by the epenthetic vowel ɨ. For example, the word ዝብርቅርቅ consists entirely of sixth order characters, and the epenthetic vowel is inserted in three places to make the word pronounceable: zɨbrɨqrɨqq. Although epenthesis is never used to distinguish words from one another, like gemination, it is important for speech synthesis.

hm.phon(language, word)
Options: gram=False

hm.phon_file(language, input_file)
Options: output_file=None

• language is a string representing the language, 'amh' for Amharic for example.

• word is a string representation of the word in the standard orthography for the language.
• input_file is a path to the file to be analyzed.

The function hm.phon takes a string abbreviation of a language and an orthographic representation of a word and prints out a romanized representation of the pronunciation of the word, including gemination and epenthesis, where appropriate. Note that there may be multiple possible pronunciations.

>>> hm.phon('amh', "ይመታል")
yɨmǝtal yɨmmǝttal

With the option gram=True, hm.phon prints out grammatical information for each pronunciation.

>>> hm.phon('amh', "ይመታል", gram=True)
-- yɨmǝtal
POS: verb, root: <mt':A>, gloss: strike,hit
 subject: 3 sing mas
 aspect/voice/tense: imperfective, aux:alle
-- yɨmmǝttal
POS: verb, root: <mt':A>, gloss: strike,hit
 subject: 3 sing mas
 aspect/voice/tense: imperfective, aux:alle, passive

With the option raw=True, a dict is returned for each pronunciation, including the internal feature structure resulting from morphological analysis.

The function hm.phon_file behaves like hm.anal_file and hm.seg_file.

Version 4.5

This is a new version of HornMorpho (HornMorphoAX), only for Amharic and only for segmentation. It currently segments tokenized sentences from a file, converting the results to CoNLL-U format, which can then be written to a file. There is also a GUI that displays the outputs and allows the user to select a segmentation for ambiguous words.

Installation

To install HornMorphoAX, use the latest wheel file, HornMorphoAX-4.5.*-py3-none-any.whl, which can be found in the dist/ folder.

To install from the wheel file, do the following in a Python shell.

pip install HornMorphoAX-4.*-py3-none-any.whl

(This assumes that you have wheel installed.)

Then to use the program, in a Python shell, do

import hm

Functions

Segmenting a sentence

hm.seg_sentence(sentence)

Options: um=1, seglevel=2

• sentence is a string representation of an Amharic sentence. It is assumed that punctuation has already be separated by whitespace from other characters.

The function hm.seg_sentence attempts to segment the word in the sentence morphologically. Words may be ambiguous, in which case there are multiple analyses. Since CoNNL-U format has no place for ambiguity, only one segmentation is included; this could be the wrong one.

This function returns an instance of the HornMorpho Sentence class. To see the CoNLL-U representation of a Sentence, call serialize() on the its conllu attribute.

Options:

• um (Universal Morphology) specifies the category of morphological features to provide for words and/or word segments. 1 (the default value): features are from the set included in the Universal Dependency guidelines. 2: features are from an extended set, possibly including those not in the basic UM set. 0: currently ignored.

• seglevel specifies how much segmentation to perform. 0: no segmentation; features are assigned to the whole word. 2: maximum segmentation; features are assigned to individual segments (morphemes) within a word. 1: currently ignored.

• gramfilter gives the name of a filter dict, which causes the function to exclude or include sentences with certain grammatical properties. An example is 'core', which excludes noun arguments that have case markers other than accusasative:

  {'out': ( (('pos', 'n'), ('featfail', FeatStruct("[prep=None]")), )}

This excludes sentences (returning None) containing a token which has POS n and anyting other than None for its prep feature.

>>> s = hm.seg_sentence("ልጁን ሥራውን አስጨርሰዋለሁ ።")
>>> print(s.conllu.serialize())

# text = ልጁን ሥራውን አስጨርሰዋለሁ ።
# sent_id = _s0
1-3	ልጁን	_	_	_	_	_	_	_	_
1	ልጅ	ልጅ	NADJ	NADJ	_	_	_	_	_
2	ኡ	ኡ	DET	DET	_	1	det	_	_
3	ን	ን	PART	ACC	Case=Acc	1	case	_	_
4-6	ሥራውን	_	_	_	_	_	_	_	_
4	ሥራ	ስራ	NADJ	NADJ	_	_	_	_	_
5	ኡ	ኡ	DET	DET	_	4	det	_	_
6	ን	ን	PART	ACC	Case=Acc	4	case	_	_
7-11	አስጨርሰዋለሁ	_	_	_	_	_	_	_	_
7	እ	እ	PRON	PRON	Number=Sing|Person=1	8	nsubj	_	_
8	አስጨርስ	ጨረሰ	VERB	VERB	Aspect=Imp|Voice=Cau	_	_	_	_
9	ኧው	ው	PRON	OBJC	Gender=Masc|Number=Sing|Person=3	8	obj	_	_
10	ኣል	ኣል	AUX	AUX	_	8	aux	_	_
11	ኧሁ	ሁ	PRON	SUBJC	Number=Sing|Person=1	8	nsubj	_	_
12	።	።	PUNCT	PUNCT	_	_	_	_	_

>>> s = hm.seg_sentence("ልጁን ሥራውን አስጨርሰዋለሁ ።", um=2, seglevel=0)
>>> print(s.conllu.serialize())

# text = ልጁን ሥራውን አስጨርሰዋለሁ ።
# sent_id = _s0
1	ልጁን	ልጅ	NOUN	NOUN	Case=Acc	_	_	_	_
2	ሥራውን	ስራ	NOUN	NOUN	Case=Acc	_	_	_	_
3	አስጨርሰዋለሁ	ጨረሰ	VERB	VERB	AccGen=Masc|AccNum=Sing|AccPers=3|Aspect=Imp|Number=Sing|Person=1|Voice=Cau	_	_	_	_
4	።	።	PUNCT	PUNCT		_	_	_	_

Segmenting the sentences in a file

hm.seg_file(path) Options: start=0, nlines=0, batch_name='', version='2.2', batch='1.0', um=1, seglevel=2

• path is a path to a file containing Amharic sentences, one per line. It is assumed that punctuation has been separated by whitespace from other characters.

The function hm.seg_file returns a list of Sentence objects.

Options:

• start specifies the line in the file where you want to start segmenting; it defaults to 0, the first line.

• n_lines specifies the number of lines to segment. It defaults to 0, meaning all of the lines.
• batch_name is a string representing the name of the batch being segmented. This is used in created the id for each sentence. If not specified, a name is created from the values of version and batch.
• version is a string or float specifying the version of the data being analyzed. It defaults to '2.2'.
• batch is a string or float specifying the batch number. It defaults to '1.0'.
• um: See seg_sentence.
• seglevel: See seg_sentence.
• gramfilter: See seg_sentence.

Writing sentence segmentations to a file

hm.write_conllu(sentences, path, corpus)

Options: unk_thresh=0.3, ambig_thresh=1.0

The function hm.write_conllu writes the CoNNL-U representations of a list of sentences (instances of Sentence) to a file or to standard output.

• sentences is a list of Sentence objects, like those returned by hm.seg_file or the sentences attribute of a Corpus object, like that returned by hm.create_corpus. If sentences is None, the sentences to be written are those in corpus.sentences.
• path is a path to the file where the CoNLL-U representations of the sentences are to be written. If None, the representations are written to the standard output.
• corpus is a Corpus object or None.

Options:

• unk_thresh and ambig_thresh control whether and how sentences are excluded from the file. unk_thresh is a float representing the maximum proportion of tokens in the sentence that are unknown to HornMorpho. It defaults to 0.3. ambig_thresh is a float representing the maximum average number of segmentations for each word beyond one. It defaults to 1.0, that is, two segmentations per word. If either of these thresholds is crossed for a given Sentence, it is not written to the file.

Creating a corpus of disambiguated sentences (starting from 4.5.1)

hm.create_corpus()

Options: start=0, n_sents=0, read={}, write={}, batch={}, segment=True, disambiguate=True, conlluify=True

hm.create_corpus() returns an instance of the Corpus class. It gets data, either from the keyword argument data, a list of sentences in the form of strings, or if data is None, from the values in the read dict. Depending on values of options, it may also segment the sentences, open the disambiguation GUI, create CoNLL-U representations, and write the CoNNL-U representations to a file.

Options:

• read is a dict with possible keys path, folder, and filename. If path is not specified, a path is created from the values of folder and filename. filename should have no extension.
• write is a dict with possible keys stdout, path, folder, filename, and annotator, specifying whether and where CoNLL-U representations of the sentences in the corpus are to be written. If stdout is True, the representations are written to standard output. Otherwise either path is used, if one is given, or, if not, a path is created from folder and filename. filename should have no extension. If degeminate is True (see below), geminated and ungeminated representations are written to separate files. If there is no filename, one is created from the batch name and the value of annotator.
• batch is a dict with possible keys name, id, sent_length, version, and source, specifying various properties of the batch being created. A batch name is created from these unless name is given.
• start specifies the number of the line (sentence) in the file where you want to start segmenting; it defaults to 0, the first line.

• n_sents specifies the number of sentences/lines to read in from the file. It defaults to 0, meaning all of the lines.
• segment specifies whether to run Corpus.segment() on the sentences (see below).
• disambiguate specifies whether to run Corpus.disambiguate() on the segmented sentences (see below).
• conlluify specifies whether to run Corpus.conlluify() on the segmented (and possibly disambiguated) sentences.
• degeminate specifies whether separate degeminated representations are created by conlluify and also written to files or standard output.

Corpus attributes

Corpus.data

A list of unsegmented Amharic sentence strings.

Corpus.sentences

A list of Sentence objects. Initially empty, filled when Corpus.segment() is called.

Corpus methods

Corpus.segment()

The Corpus method segment() runs hm.seg_sentence() on the sentence strings in the data attribute, saving the resulting Sentence objects in the sentences attribute. Each of these has a conllu field, which can be converted to a CoNLL-U string with serialize(). But for words that are ambiguous to HornMorpho, the CoNLL-U representations simply take the first of the segmentations that are output for words. To have control over this process, see the next method, Corpus.disambiguate().

Example:

>>> c = hm.create_corpus(read={'path': "hm/ext_data/CACO/CACO1.1/CACO_TEXT_3-7tok.txt"}, n_sents=2, segment=False)
                        
>>> c.data
['አሁን ወደ ዋናው የጉዞ ፕሮግራም እንመለስ ።', 'ስለሚሉት ጉዳዮች ማወቅ አለባቸው ።']
>>>c.sentences
[]
>>> c.segment()
Segmenting sentences in C_CACO2.2_B1.0
>>> c.sentences
[S0::አሁን ወደ ዋናው የጉዞ ፕሮግራም እንመለስ ።, S1::ስለሚሉት ጉዳዮች ማወቅ አለባቸው ።]

Corpus.disambiguate()

The Corpus method disambiguate(), which is called by create_corpus() if disambiguate is True, opens a GUI that displays the segmentations returned by HornMorpho for each word in each sentence in the corpus's sentences attribute. For ambiguous words, that is, words for which HornMorpho returns more than one segmentation, the GUI permits selecting one of the segmentations. The possible modified segmentations are saved in the conllu attribute of the relevant Sentence object when the GUI is exited. If Corpus.sentences is empty, Corpus.segment() is run before the GUI is opened.

Here is an image of the GUI.

At the top of the window are buttons and text fields for selecting particular sentences or words. (The arrow keys can also be used to advance to the next word or return to the previous one.) The current sentence is shown in the space below the buttons, with the current word underlined. The sentence's label is shown above it. If the sentence contains no ambiguities, the label and the background behind the sentence are gray. Otherwise words within a sentence that are unambiguous are displayed with gray backgrounds, in the example in the figure all of the words, except the second and fifth. Segmentations of the current word are shown in the space below. Each segmentation appears in a box, with the segments (morphemes) arranged in columns. At the top of the segmentation, the dependencies between segments are shown. Below this each column gives the form, POS tags (if UPOS and XPOS are different, both are given), features if any, and lemmas, if any are different from the forms.

If the word is ambiguous, that is, if there is more than one segmentation, a number appears to the left of each segmentation box. To select one segmentation, click on the number. You should then see only the segmentation you selected. Selection changes the representation of the word in the Corpus instance that created the GUI. The background color for word in the sentence text field is then changed to green, indicating that its segmentation has been edited by the user. In the figure, the user has selected one segmentation for the current word, ጡንቻዎቹ.

If a word's or segment's POS is ambiguous, two options may be shown highlighted in pink. Clicking on one of these selects it as the POS.

There is an "Undo" bottom at the top. To undo one or more actions made, either selections of segmentations or of POS tags, go to the word whose segmentation or POS choice you want to undo, and click this button. The "Undo" button is disabled when the current word has not been edited.

To quit the GUI, click on the "Quit" button in the upper right.

Corpus.conlluify()

Option: degeminate=False

The Corpus method conlluify() creates a new CoNLL-U representation for each of the Sentence instances stored in the corpus's sentences attribute. You would normally call this method after running disambiguate() on the sentences. If the option degeminate is True, separate geminated and ungeminated representations are created. In the degeminated versions, the Geez gemination character is removed from all lemmas. (Forms are already degeminated.)

In summary, here's an example of how to create a corpus of two sentences, segment and disambiguate the sentences, create CoNLL-U representations for the sentences, both geminated and ungeminated, and write these to two files.

>>> hm.create_corpus(
    read={'folder': "../../TAFS/datasets/CACO", 'filename': "CACO_3-7tok_B2"},
    batch={'n_sents': 2, 'sent_length': '3-7'},
    degeminate=True,
    write={'folder': CONLLU}
    )
Segmenting sentences in C_CACO_3-7_B1_2
Conlluifying sentences in C_CACO_3-7_B1_2
Writing CoNLL-U sentences C_CACO_3-7_B1_2 to ../../TAFS/venv/conllu/CACO_3-7_B1_2_A1-G.conllu
Writing CoNLL-U sentences C_CACO_3-7_B1_2 to ../../TAFS/venv/conllu/CACO_3-7_B1_2_A1-U.conllu